related to PIK research on Time Series Analysis and Complex Networks

Recurrence plots

Recurrence plots (RPs) provide an alternative way to study various aspects of complex systems, such regime transitions, classification, detection of time-scales, synchronisation, and coupling detection (RP bibliography). Main contributions have been in bivariate extensions (cross RPs) and coupling analysis, new measures of complexity, significance assessments of the RP based results, spatial extensions, parameter selection, RPs for irregularly sampled data and for extreme events data, or complex network based quantification.

• N. Marwan, M. C. Romano, M. Thiel and J. Kurths: Recurrence Plots for the Analysis of Complex Systems, Physics Reports, 438(5–6), 237–329 (2007). DOI:10.1016/j.physrep.2006.11.001
  
• N. Marwan, J. F. Donges, Y. Zou, R. V. Donner and J. Kurths: Complex network approach for recurrence analysis of time series, Physics Letters A, 373(46), 4246–4254 (2009). DOI:10.1016/j.physleta.2009.09.042
  
• N. Marwan: How to avoid potential pitfalls in recurrence plot based data analysis, International Journal of Bifurcation and Chaos, 21(4), 1003–1017 (2011). DOI:10.1142/S0218127411029008
  
• N. Marwan, S. Schinkel and J. Kurths: Recurrence plots 25 years later – Gaining confidence in dynamical transitions, Europhysics Letters, 101, 20007 (2013). DOI:10.1209/0295-5075/101/20007
  
• T. Braun, V. R. Unni, R. I. Sujith, J. Kurths and N. Marwan: Detection of dynamical regime transitions with lacunarity as a multiscale recurrence quantification measure, Nonlinear Dynamics, 104, 3955–3973 (2021). DOI:10.1007/s11071-021-06457-5
  
• N. Marwan: Challenges and perspectives in recurrence analyses of event time series, Frontiers in Applied Mathematics and Statistics, 9, 1129105 (2023). DOI:10.3389/fams.2023.1129105
  

Complex networks

Complex networks provide a powerful approach for analyzing extended and spatio-temporal systems, such as the climate by climate networks. We introduced the concept of event synchronisation into climate network analysis to enabling targeted studies of extreme events. Moreover, they offer an alternative way for a recurrence based time-series analysis by recurrence networks.

• N. Malik, N. Marwan and J. Kurths: Spatial structures and directionalities in Monsoonal precipitation over South Asia, Nonlinear Processes in Geophysics, 17(5), 371–381 (2010). DOI:10.5194/npg-17-371-2010
  
• N. Boers, B. Bookhagen, N. Marwan, J. Kurths and J. Marengo: Complex networks identify spatial patterns of extreme rainfall events of the South American Monsoon System, Geophysical Research Letters, 40(16), 4386–4392 (2013). DOI:10.1002/grl.50681
  
• K. Rehfeld, N. Marwan, S. F. M. Breitenbach and J. Kurths: Late Holocene Asian summer monsoon dynamics from small but complex networks of paleoclimate data, Climate Dynamics, 41(1), 3–19 (2013). DOI:10.1007/s00382-012-1448-3
  
• N. Marwan, S. Foerster and J. Kurths: Analysing spatially extended high-dimensional dynamics by recurrence plots, Physics Letters A, 379(10–11), 894–900 (2015). DOI:10.1016/j.physleta.2015.01.013
  
• Y. Zou, R. V. Donner, N. Marwan, J. F. Donges and J. Kurths: Complex network approaches to nonlinear time series analysis, Physics Reports, 787, 1–97 (2019). DOI:10.1016/j.physrep.2018.10.005
  

Special time-series analysis methods for special problems

Special problems require especially adopted methods of time-series analysis. For example, proxy records in Earth sciences are often irregularly sampled and come with uncertainties in the dating points. Approaches for considering such dating uncertainties in the subsequent analysis and methods for correlation analysis of irregularly sampled time series have been developed. Such approaches can be helpful for the reconstruction of palaeoclimate complex networks.

• K. Rehfeld, N. Marwan, J. Heitzig and J. Kurths: Comparison of correlation analysis techniques for irregularly sampled time series, Nonlinear Processes in Geophysics, 18(3), 389–404 (2011). DOI:10.5194/npg-18-389-2011
  
• S. F. M. Breitenbach, K. Rehfeld, B. Goswami, J. U. L. Baldini, H. E. Ridley, D. Kennett, K. Prufer, V. V. Aquino, Y. Asmerom, V. J. Polyak, H. Cheng, J. Kurths and N. Marwan: COnstructing Proxy-Record Age models (COPRA), Climate of the Past, 8, 1765–1779 (2012). DOI:10.5194/cp-8-1765-2012
  
• D. Eroglu, F. H. McRobie, I. Ozken, T. Stemler, K.-H. Wyrwoll, S. F. M. Breitenbach, N. Marwan and J. Kurths: See-saw relationship of the Holocene East Asian-Australian summer monsoon, Nature Communications, 7, 12929 (2016). DOI:10.1038/ncomms12929
  
• B. Goswami, N. Boers, A. Rheinwalt, N. Marwan, J. Heitzig, S. F. M. Breitenbach and J. Kurths: Abrupt transitions in time series with uncertainties, Nature Communications, 9, 48 (2018). DOI:10.1038/s41467-017-02456-6
  
• I. Ozken, D. Eroglu, S. F. M. Breitenbach, N. Marwan, L. Tan, U. Tirnakli and J. Kurths: Recurrence plot analysis of irregularly sampled data, Physical Review E, 98, 052215 (2018). DOI:10.1103/PhysRevE.98.052215
  
• N. Marwan and T. Braun: Power spectral estimate for discrete data, Chaos, 33(5), 053118 (2023). DOI:10.1063/5.0143224
  

Climate and palaeoclimate

We use advanced data analysis methods, e.g., complex networks, to study spatio-temporal climate systems. One key product is the PIKART catalogue, the world-leading catalogue of atmospheric rivers. The study of palaeoclimate from proxy records is helpful for a better understanding of the climate system. Information based on lake sediments or speleothemes can be used to study complex interrelationships or past climate transitions. We are also participating in the coordinated scientific research in the Blessberg Cave, Thuringia.

• J. F. Donges, R. V. Donner, M. H. Trauth, N. Marwan, H. J. Schellnhuber and J. Kurths: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution, Proceedings of the National Academy of Sciences, 108(51), 20422–20427 (2011). DOI:10.1073/pnas.1117052108
  
• D. J. Kennett, S. F. M. Breitenbach, V. V. Aquino, Y. Asmerom, J. Awe, J. U. L. Baldini, P. Bartlein, B. J. Culleton, C. Ebert, C. Jazwa, M. J. Macri, N. Marwan, V. Polyak, K. M. Prufer, H. E. Ridley, H. Sodemann, B. Winterhalder and G. H. Haug: Development and Disintegration of Maya Political Systems in Response to Climate Change, Science, 338(6108), 788–791 (2012). DOI:10.1126/science.1226299
  
• N. Boers, B. Bookhagen, H. M. J. Barbosa, N. Marwan, J. Kurths and J. A. Marengo: Prediction of extreme floods in the eastern Central Andes based on a complex networks approach, Nature Communications, 5, 5199 (2014). DOI:10.1038/ncomms6199
  
• A. Agarwal, L. Caesar, N. Marwan, R. Maheswaran, B. Merz and J. Kurths: Network-based identification and characterization of teleconnections on different scales, Scientific Reports, 9, 8808 (2019). DOI:10.1038/s41598-019-45423-5
  
• M. Singh, R. Krishnan, B. Goswami, A. D. Choudhury, P. Swapna, R. Vellore, A. G. Prajeesh, N. Sandeep, C. Venkataraman, R. V. Donner, N. Marwan and J. Kurths: Fingerprint of volcanic forcing on the ENSO–Indian monsoon coupling, Science Advances, 6, eaba8164 (2020). DOI:10.1126/sciadv.aba8164
  
• T. Westerhold, N. Marwan, A. J. Drury, D. Liebrand, C. Agnini, E. Anagnostou, J. S. K. Barnet, S. M. Bohaty, D. De Vleeschouwer, F. Florindo, T. Frederichs, D. A. Hodell, A. E. Holbourn, D. Kroon, V. Lauretano, K. Littler, L. J. Lourens, M. Lyle, H. Pälike, U. Röhl, J. Tian, R. H. Wilkens, P. A. Wilson and J. C. Zachos: An astronomically dated record of Earth's climate and its predictability over the last 66 million years, Science, 369(6509), 1383–1387 (2020). DOI:10.1126/science.aba6853
  
• S. M. Vallejo-Bernal, T. Braun, N. Marwan and J. Kurths: PIKART: A Comprehensive Global Catalog of Atmospheric Rivers, Journal of Geophysical Research: Atmospheres, 130(15), e2024JD041869 (2025). DOI:10.1029/2024JD041869
  

Cardiovascular systems

Besides the main focus on climate related problems, recurrence properties of the cardiovascular system are studied, e.g., to early detect ventricular tachycardia or preeclampsia, or to investigate the coupling mechanisms in the cardio-respiratory system.

• N. Marwan, N. Wessel, U. Meyerfeldt, A. Schirdewan and J. Kurths: Recurrence Plot Based Measures of Complexity and its Application to Heart Rate Variability Data, Physical Review E, 66(2), 026702 (2002). DOI:10.1103/PhysRevE.66.026702
  
• N. Marwan, Y. Zou, N. Wessel, M. Riedl and J. Kurths: Estimating coupling directions in the cardio-respiratory system using recurrence properties, Philosophical Transactions of the Royal Society A, 371(1997), 20110624 (2013). DOI:10.1098/rsta.2011.0624
  
• G. M. Ramírez Ávila, A. Gapelyuk, N. Marwan, H. Stepan, J. Kurths, T. Walther and N. Wessel: Classifying healthy women and preeclamptic patients from cardiovascular data using recurrence and complex network methods, Autonomic Neuroscience, 178(1–2), 103–110 (2013). DOI:10.1016/j.autneu.2013.05.003
  

Neuroscience

Further interest in life science is related to EEG analysis, aiming at the detection of event related potentials or early signatures of epileptic seizures, or identifying pathological changes in brains connectivity due to diseases.

• N. Marwan and A. Meinke: Extended recurrence plot analysis and its application to ERP data, International Journal of Bifurcation and Chaos, 14(2), 761–771 (2004). DOI:10.1142/S0218127404009454
  
• S. Schinkel, N. Marwan and J. Kurths: Order patterns recurrence plots in the anaylsis of ERP data, Cognitive Neurodynamics, 1(4), 317–325 (2007). DOI:10.1007/s11571-007-9023-z
  
• S. Schinkel, N. Marwan and J. Kurths: Brain signal analysis based on recurrences, Journal of Physiology-Paris, 103(6), 315–323 (2009). DOI:10.1016/j.jphysparis.2009.05.007
  
• E. J. Ngamga, S. Bialonski, N. Marwan, J. Kurths, C. Geier and K. Lehnertz: Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data, Physics Letters A, 380(16), 1419–1425 (2016). DOI:10.1016/j.physleta.2016.02.024
  
• M. Mannone, P. Fazio and N. Marwan: Modeling a neurological disorder as the result of an operator acting on the brain: A first sketch based on network channel modeling, Chaos, 34, 053133 (2024). DOI:10.1063/5.0199988
  

3D image analysis

Methods to investigate complexity in 3D have been applied to study structural changes in trabecular bone, such as occurring during osteoporosis or space flights.

• N. Marwan, P. Saparin and J. Kurths: Measures of complexity for 3D image analysis of trabecular bone, European Physical Journal – Special Topics, 143(1), 109–116 (2007). DOI:10.1140/epjst/e2007-00078-x
  
• N. Marwan, J. Kurths, J. S. Thomsen, D. Felsenberg and P. Saparin: Three dimensional quantification of structures in trabecular bone using measures of complexity, Physical Review E, 79(2), 021903 (2009). DOI:10.1103/PhysRevE.79.021903
  
• T. Schmah, N. Marwan, J. S. Thomsen and P. Saparin: Long range node-strut analysis of trabecular bone microarchitecture, Medical Physics, 38(9), 5003–5011 (2011). DOI:10.1118/1.3622600
  

Scientific research in caves is performed to explore and survey newly discovered cave parts, but also to collect data for the palaeoclimate studies (samples, monitoring). Cave research is focused on caves in Switzerland (research with ISAAK), but also in India, Caucasus, Kosovo, and Germany.

• N. Marwan: Cave Blisters in der Oberländerhöhle (M3)/ Découverte de blisters dans la Oberländerhöhle (M3), Stalactite, 50(2), 103–105 (2000)
  
• N. Marwan: Kalzit-Sinter in Sandsteinhöhlen des Elbsandsteingebirges, Die Höhle, 51(1), 19-20 (2000)
  
• N. Marwan: Das Karstgebiet des Bol'šoj Tha\v c, Abhandlungen und Berichte des Naturkundemuseums Görlitz, 79(1), 55-84 (2007)
  
• S. Breitenbach, N. Marwan and G. Wibbelt: Weißnasensyndrom in Nordamerika – Pilzbesiedlung in Europa, Nyctalus, 16(3), 172–179 (2011)
  
• N. Marwan: Der digitale Sägistal-Kataster, Stalactite, 73(1), 24–33 (2023)
  
• S. F. M. Breitenbach and N. Marwan: Using Low-Cost Software to Obtain and Study Stalagmite Greyscale Data, CREG Journal, 125, 7–10 (2024)
  

• one of the first web presentations of speleology was the speleo server east

Sort by year | funding body | subject

• 2000–2003
  DFG SPP 1097: Geomagnetic Variations: Nonlinear Phase and Correlation Analysis of Palaeomagnetic and Palaeoclimatic Records

• 2003–2005
  ESA project AO-99-030: 2D and 3D Quantification of Bone Structure and its Changes in Microgravity Condition by Measures of Complexity

• 2005–2008
  ESA project AO-2004-125: Assessing the Influence of Microarchitecture on the Mechanical Performance of Bone and its Changes in Microgravity from in-vivo Measurements

• 2006–2015
  DFG Graduate School GK 1364: Shaping Earth's surface in a variable environment

• 2010
  Volkswagen Foundation I/85 116: International Workshop on Recent Achievements on the Study of Extreme Events

• 2010–2013
  Leibniz WGL SAW-2010: Evolving Complex Networks (ECONS) — Regional resource management under environmental and demographic change

• 2011–2014
  DFG Graduate School GK 1539: Sichtbarkeit und Sichtbarmachung, Hybride Formen des Bildwissens

• 2011–2013
  DFG Research Group FOR 1380: Himalaya: Modern and Past Climates (HIMPAC): Analysis of the dynamics of palaeo and modern climate data under consideration of dating errors focussed on climate transitions and interrelations between teleconnections and regional climate

• 2011–2013
  DFG project KU837/34-1: Interactions and complex structures in the dynamics of changing climate: impact of tipping elements in presence and past

• 2011–2014
  BMBF Spitzenforschung und Innovation in den Neuen Ländern: Potsdam Research Cluster for Georisk Analysis, Environmental Change and Sustainability (PROGRESS): Extremereignisse in Geoarchiven

• 2013–2016
  Leibniz WGL SAW-2013-IZW-2: Gradual environmental change versus single catastrophe — Identifying drivers of mammalian evolution

• 2014–2015
  DFG project MA 4759/4-1: Investigation of past and present climate dynamics and its stability by means of a spatio-temporal analysis of climate data using complex networks

• 2015–2024
  DFG Graduate School GK 2043: Natural Hazards and Risks in a Changing World (NatRiskChange)

• 2016–2020
  EU project H2020-MSCA-RISE-2015: QUantitative palaeoEnvironments from SpeleoThems (QUEST)

• 2017–2019
  DFG project 337352542: Trends, rhythms and events in East African climate: statistical analysis of the paleoclimare records of the long sediment cores of the Chew Bahir basin

• 2017–2022
  DFG project MA4759/8-1: Impacts of uncertainties in climate data analyses (IUCliD): Approaches to working with measurements as a series of probability distributions

• 2017–2020
  DFG project MA4759/9-1: Recurrence plot analysis of regime changes in dynamical systems

• 2019–2023
  DFG project MA4759/11-1: Nonlinear empirical mode analysis of complex systems: Development of general approach and application in climate

• 2019–2024
  EU project 820970: Tipping points in the earth system (TiPES)

• 2020–2024
  BMBF project synXtreme: Spatial synchronization patterns of extreme heavy precipitation events in Europe, within the climXtreme research network on climate change and extreme events

• 2022–2023
  DFG project MA 4759/18-1: Testing the isothermal thermoluminescence dating method to constrain mid-Pleistocene speleothem growth phases in the Bleßberg Cave

• 2023
  DFG project MA 4759/19-1: International scientific conference: „Nonlinear Data Analysis and Modeling: Advances, Applications, Perspectives (NDA23)“, Potsdam

• 2023
  geo.X grant Grow Your Idea!: Geo.X Brainstorm Meeting: „Reconstructing Environmental Changes in the Swiss Alps“, Potsdam

• 2024–2026
  DAAD grant 57705568: PPP with Brazil CAPES „Recurrence quantifiers as features for machine-learning-decision-making processes“

• 2026–
  EU sunrise: Extreme events in a warming, unequal world: Linking drivers to impacts for improved attribution, forecasting and regional responses (SUNRISE)

• 2026–
  Leibniz scales: Leibniz ScienceCampus SCALES: Multiscale Challenges in Physics: From Astrophysics to Climate Modeling

Internships

1. Abhishek Dasgupta, Internship, University of Potsdam and Indian Institute of Technology Kharagpur, 2006
   Bi-dimensional Empirical Mode Decomposition on Bone Images, Quantification of cancellous bone structure using symbolic dynamics and measures of Complexity.

2. Nadezhda Zolotova, Internship, University of Potsdam and St. Petersburg State University, 2007
   Synchronization phenomena at the Sun and in the solar corona

3. Michael B. Sexton, Erasmus Internship, University of Potsdam and Trinity College Dublin, 2008
   The spatial resolution dependence of complex climate networks

4. Scott McKechnie, Erasmus Internship, University of Potsdam and Trinity College Dublin, 2009
   Complex Network Analysis of Tipping Elements of the Climate System

5. Alraune Zech, Internship, Potsdam Institut for Climate Impact Research, 2009
   Reconstructing time series from given networks

6. Bedartha Goswami, Internship, Indian Institute of Science Education and Research Pune, 2010
   Recurrence based interrelation measures » publications

   1. B. Goswami, G. Ambika, N. Marwan and J. Kurths: On interrelations of recurrences and connectivity trends between stock indices, Physica A, 391(), 4364–4376 (2012). DOI:10.1016/j.physa.2012.04.018

7. Timo Wildhage, Internship, TU Berlin, 2012-2013
   Analysis of snowline elevation time series for Central Asia

8. Jonas Knapp, University of Cambridge, 2015
   Recurrence coupling analysis of electricity market and weather

9. Sam Reed, Internship, University of Cambridge, 2016
   

10. Yu-Xuan Yang, Internship, Tianjin University, 2016
    

11. Thorsten Becker, Internship, TU Berlin, 2017
    Correlation analysis of Bleßberg Cave proxy data

12. Vedasri Godavarthi, Indian Institute of Technology Madras, Chennai, 2017
    Complex network analysis of flow data (combustion process/ simulation)

13. María Fernanda Ayala Cajas, Universidad de las Fuerzas Armadas ESPE, Ecuador, 2017
    Analysis of the Chaotic Dynamics for the time series of meteorological variables in the climatological station of Chone, Ecuador

14. Abin Krishnan, Indian Institute of Technology Madras, Chennai, 2018
    

15. Laura Bergmann, Katholische Uni Ingolstadt-Eichstätt, 2021
    Höhlenkarbonatdatierungen in Zentraleuropa

16. Lina Stritt, University of Manchester, 2023
    Python implementation speleothem age distributions

17. Patrick Selleng, Fachhochschule Magdeburg-Stendal, 2023/24
    Distance metrices for recurrence analysis

18. Julia Omelchenko, Humboldt University Berlin, 2025
    Edit distance spectra of Geysers

19. Gabriela Rafałko, Bialystok University of Technology, 2025
    Recurrence Analysis of EWOD data

20. Rosa Gutierrez, Universidad Politécnica de Madrid (UPM), 2025
    Spatial analysis of grassland MODIS data (NDVI) using recurrence approaches

21. Diogo de Souza, State University of Ponta Grossa, Brazil, 2025/26
    Bistability and intermittency in neuronal networks during spontaneous seizure generation

22. Paul Neumeier, University of Leipzig, 2026
    SfM reconstruction of sodastraws from Blessberg cave

23. Max Meyer, University of Paderborn, 2026
    Quantum computing and recurrence plots

Diploma/ Master/ Bachelor

1. Sabrina Donner, Diploma, University of Potsdam, Physics, 2009
   Numerische Verfahren zur Approximation von Sattelpunkten: Vergleich und Anwendung auf GCM-Daten

2. Stefan Schinkel, Diploma, University of Potsdam, Psychology, 2005-2006
   EKP Untersuchung mit Hilfe der quantitativen Analyse von Recurrence Plots

3. Nils Haug, Bachelor, Humboldt University Berlin, 2010
   Spatiotemporal analysis of precipitation in the Himalaya region

4. Andreas Müller, Diploma, University of Potsdam, Physics, 2010
   Kausalitätsuntersuchungen in komplexen Systemen

5. Hannes Kutza, Master, Humboldt University Berlin, Physics, 2011
   Pattern recognition in complex networks based on spatially embedded time series

6. Aljoscha Rheinwalt, Diploma, Humboldt University Berlin, Physics, 2011
   Rainfall Networks Precipitation analysis for the region of Germany using complex networks

7. Jan H. Feldhoff, Diploma, Humboldt University Berlin, Physics, 2011
   Multivariate extensions to recurrence network analysis » publications

   1. J. H. Feldhoff, R. V. Donner, J. F. Donges, N. Marwan and J. Kurths: Geometric detection of coupling directions by means of inter-system recurrence networks, Physics Letters A, 376(46), 3504–3513 (2012). DOI:10.1016/j.physleta.2012.10.008
   2. J. H. Feldhoff, R. V. Donner, J. F. Donges, N. Marwan and J. Kurths: Geometric signature of complex synchronisation scenarios, Europhysics Letters, 102(3), 30007 (2013). DOI:10.1209/0295-5075/102/30007

8. Jan Wohland, Bachelor, Humboldt University Berlin, 2013
   Influence of grid resolution/setting on climate networks results

9. Carl Witt, Master, GFZ/ Humboldt University Berlin, Informatics, 2015
   Clustering of Recurrence Plots » publications

   1. P. Köthur, C. Witt, M. Sips, N. Marwan S. Schinkel and D. Dransch: Visual Analytics for Correlation-Based Comparison of Time Series Ensembles, Computer Graphics Forum, 34(3), 411–420 (2015). DOI:10.1111/cgf.12653
   2. M. Sips, C. Witt, T. Rawald and N. Marwan: Torwards Visual Analytics for the Exploration of Large Sets of Time Series, In: Recurrence Plots and Their Quantifications: Expanding Horizons, Eds.: C. L. Webber, Jr. and C. Ioana and N. Marwan, Springer, 3–17 (2016). DOI:10.1007/978-3-319-29922-8_1

10. Malte Viefhues, Bachelor, Humboldt University Berlin, Physics, 2014-2016
    Analysis of Spatial Patterns in STARS-Generated Data using Climate Networks and 𝜅-Statistic

11. Johannes Donath, Bachelor, Humboldt University Berlin, Physics, 2015-2016
    Untersuchung alternativer Zeitfensterformen für die quantitative Rekurrenzanalyse anhand von Modellsystemen und Klimadaten

12. David Salomon, Diploma, Humboldt University Berlin, Informatics, 2016
    Bestimmung der Recurrence Matrix für die Recurrence Quantification Analysis mittels Approximate Nearest Neighbor Search

13. Johannes Donath, Master, Humboldt University Berlin, Physics, 2018-2019
    Line definitions in RPs with uncertainties

14. Georg Klinghammer, Master, Humboldt University Berlin, Physics, 2019-2022
    Recurrence characteristics of different types of tipping points

15. J. M., Master, Humboldt University Berlin, Physics, 2020-(cancelled)
    Impact of time series sampling on recurrence analysis

16. Jamir Priesner, MA, University of Saarland, Saarbrücken, 2021–2023
    Biophysical Coupling of Climate and Vegetation in the Amazon Region studied with Complex Network Analysis

17. Nils Antary, MA, University of Leipzig, Physics, 2022–2024
    Influence of interpolation on recurrence quantification analysis » publications

    1. N. Antary, M. H. Trauth and N. Marwan: Interpolation and sampling effects on recurrence quantification measures, Chaos, 33, 103105 (2023). DOI:10.1063/5.0167413

18. J. G., MA, Humboldt University Berlin, 2022–(cancelled)
    Vorticity networks

19. Jurij Schönfeld, MA, University of Potsdam, Physics, 2023–2024
    Dynamic Contact Network Generation for Improved Epidemic Modeling

20. Adrian Focke, BA, H2 Uni Magdeburg-Stendal, 2024
    Analyse unregelmäßig abgetasteter Zeitreihen durch stückweise konstante Approximation

21. Lütje Lange, MA, University of Potsdam, Physics, 2024–2025
    Spectral Analysis of event-based time-series

22. Mira Suhrhoff, MA, University of Potsdam, ClEWS, 2024–
    

23. David Boukroum, MA, Humboldt University Berlin, Mathematics, 2024–2025
    Statistical Early-Warning under Regimes of Periodic Forcing

24. Cara Bielig, MA, University of Potsdam, Remote Sensing, 2025–2026
    Exploring Spatial Complexity Using Extended Recurrence Plot Analysis

25. Franziska Niederstadt, MA, TU Berlin, Physics, 2024–2026
    Recurrence Analysis for Event Data -- Advances in Joint Recurrence Analysis of Heterogeneous Data

26. Jack Ross, BA, Uni Cork, 2024–2025
    Surrogate Data for Chaotic Systems: A Reservoir Computing Approach

27. Moritz Everwien, MA, University of Potsdam, Physics, 2024–2026
    Transitions of the Indian Monsoon during the Holocene

28. Jakob Eisenhauer, BA, Humboldt University Berlin, Physics, 2025
    

29. Nici Scheel, BA, University of Potsdam, Physics, 2025–2026
    Bibliometrische Analyse von Recurrence-Plot-Papern im Bereich Maschinelles Lernen

30. Florian Bakenecker, MA, Humboldt University Berlin, Physics, 2025–
    

31. Catharina Vanelli, MA, University of Potsdam, ClEWS, 2024–2025
    Modelling moisture-vegetation feedbacks in Amazonia – Linking Deforestation, Droughts, and Indigenous Peoples' Lands

32. Mia Janzen, MA, University of Potsdam, Remote Sensing, 2025–
    

33. Ben Lefebvre, MA, University of Potsdam, Remote Sensing, 2025–2026
    Detection and Tracking of Extreme Weather Events Using GNSS-Based Water Vapour Time Series Analysis

34. Martyna Stawna, MA, FU Berlin, Mathematics, 2026–
    

35. Matthias Böhl, MA, University of Potsdam, Mathematics, 2025–
    

36. Benedict Steil, BA, Freie Universität Berlin, Physics, 2026–
    

37. Malte Springer, MA, University of Potsdam, Remote Sensing, 2026–
    

38. Wonjin Kim, MA, University of Potsdam, Remote Sensing, 2026–
    

PhD

1. Marco Rusconi, PhD, University of Potsdam, Physics, 2005-2008 (co-supervision)
   New theoretical approaches to bone remodelling: Markov processes and stochastic resonance » publications

   1. M. Rusconi, A. Zaikin, N. Marwan and J. Kurths: Effect of Stochastic Resonance on Bone Loss in Osteopenic Conditions, Physical Review Letters, 100(12), 128101 (2008). DOI:10.1103/PhysRevLett.100.128101

2. Naoki Itoh, PhD, University of Potsdam, Physics, ?-2013 (co-supervision)
   Applications of developed and extended singular spectrum analysis to monthly precipitation » publications

   1. N. Itoh and N. Marwan: An extended singular spectrum transformation (SST) for the investigation of Kenyan precipitation data, Nonlinear Processes in Geophysics, 20, 467–481 (2013). DOI:10.5194/npg-20-467-2013

3. Stefan Schinkel, PhD, University of Potsdam, 2006-2010 (co-supervision)
   Single trial analysis of event-related potentials – a recurrence-based approach » publications

   1. S. Schinkel, N. Marwan and J. Kurths: Order patterns recurrence plots in the anaylsis of ERP data, Cognitive Neurodynamics, 1(4), 317–325 (2007). DOI:10.1007/s11571-007-9023-z
   2. S. Schinkel, O. Dimigen and N. Marwan: Selection of recurrence threshold for signal detection, European Physical Journal – Special Topics, 164(1), 45–53 (2008). DOI:10.1140/epjst/e2008-00833-5
   3. S. Schinkel, N. Marwan, O. Dimigen and J. Kurths: Confidence bounds of recurrence-based complexity measures, Physics Letters A, 373(26), 2245–2250 (2009). DOI:10.1016/j.physleta.2009.04.045
   4. S. Schinkel, N. Marwan and J. Kurths: Brain signal analysis based on recurrences, Journal of Physiology-Paris, 103(6), 315–323 (2009). DOI:10.1016/j.jphysparis.2009.05.007

4. Jonathan Donges, PhD, Humboldt University Berlin, Physics, 2009-2012 (co-supervision)
   Functional network macroscopes for probing past and present Earth system dynamics: Complex hierarchical interactions, tipping points, and beyond » publications

   1. J. F. Donges, Y. Zou, N. Marwan and J. Kurths: The backbone of the climate network, Europhysics Letters, 87, 48007 (2009). DOI:10.1209/0295-5075/87/48007
   2. J. F. Donges, Y. Zou, N. Marwan and J. Kurths: Complex networks in climate dynamics – Comparing linear and nonlinear network construction methods, European Physical Journal – Special Topics, 174, 157–179 (2009). DOI:10.1140/epjst/e2009-01098-2
   3. Y. Zou, R. V. Donner, J. F. Donges, N. Marwan and J. Kurths: Identifying complex periodic windows in continuous-time dynamical systems using recurrence-based methods, Chaos, 20(4), 043130 (2010). DOI:10.1063/1.3523304
   4. R. V. Donner, J. F. Donges, Y. Zou, N. Marwan and J. Kurths: Recurrence-Based Evolving Networks for Time Series Analysis of Complex Systems, 87–90 (2010).
   5. R. V. Donner, Y. Zou, J. F. Donges, N. Marwan and J. Kurths: Ambiguities in recurrence-based complex network representations of time series, Physical Review E, 81, 015101(R) (2010). DOI:10.1103/PhysRevE.81.015101
   6. R. V. Donner, Y. Zou, J. F. Donges, N. Marwan and J. Kurths: Recurrence networks – A novel paradigm for nonlinear time series analysis, New Journal of Physics, 12(3), 033025 (2010). DOI:10.1088/1367-2630/12/3/033025
   7. J. F. Donges, R. V. Donner, K. Rehfeld, N. Marwan, M. H. Trauth and J. Kurths: Identification of dynamical transitions in marine palaeoclimate records by recurrence network analysis, Nonlinear Processes in Geophysics, 18, 545–562 (2011). DOI:10.5194/npg-18-545-2011
   8. J. F. Donges, R. V. Donner, M. H. Trauth, N. Marwan, H. J. Schellnhuber and J. Kurths: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution, Proceedings of the National Academy of Sciences, 108(51), 20422–20427 (2011). DOI:10.1073/pnas.1117052108
   9. J. F. Donges, H. C. H. Schultz, N. Marwan, Y. Zou and J. Kurths: Investigating the topology of interacting networks – Theory and application to coupled climate subnetworks, European Physical Journal B, 84, 635–651 (2011). DOI:10.1140/epjb/e2011-10795-8
   10. R. V. Donner, M. Small, J. F. Donges, N. Marwan, Y. Zou, R. Xiang and J. Kurths: Recurrence-based time series analysis by means of complex network methods, International Journal of Bifurcation and Chaos, 21(4), 1019–1046 (2011). DOI:10.1142/S0218127411029021
   11. R. V. Donner, J. Heitzig, J. F. Donges, Y. Zou, N. Marwan and J. Kurths: The Geometry of Chaotic Dynamics – A Complex Network Perspective, European Physical Journal B, 84, 653–672 (2011). DOI:10.1140/epjb/e2011-10899-1
   12. Y. Zou, J. Heitzig, R. V. Donner, J. F. Donges, J. D. Farmer, R. Meucci, S. Euzzor, N. Marwan and J. Kurths: Power-laws in recurrence networks from dynamical systems, Europhysics Letters, 98, 48001 (2012). DOI:10.1209/0295-5075/98/48001

5. Nishant Malik, PhD, University of Potsdam, Physics, 2008-2012 (co-supervision)
   Extremes in events and dynamics: a nonlinear data analysis perspective on the past and present dynamics of the Indian summer monsoon. » publications

   1. N. Malik, N. Marwan and J. Kurths: Spatial structures and directionalities in Monsoonal precipitation over South Asia, Nonlinear Processes in Geophysics, 17(5), 371–381 (2010). DOI:10.5194/npg-17-371-2010
   2. N. Malik, Y. Zou, N. Marwan and J. Kurths: Dynamical regimes and transitions in Plio-Pleistocene Asian monsoon, Europhysics Letters, 97(4), 40009 (2012). DOI:10.1209/0295-5075/97/40009
   3. N. Malik, B. Bookhagen, N. Marwan and J. Kurths: Analysis of spatial and temporal extreme monsoonal rainfall over South Asia using complex networks, Climate Dynamics, 39(3–4), 971–987 (2012). DOI:10.1007/s00382-011-1156-4
   4. N. Malik, N. Marwan, Y. Zou, P. J. Mucha and J. Kurths: Fluctuation of similarity to detect transitions between distinct dynamical regimes in short time series, Physical Review E, 89, 062908 (2014). DOI:10.1103/PhysRevE.89.062908

6. Kira Rehfeld, PhD, Humboldt University Berlin, Physics, 2010-2013 (co-supervision)
   Embracing nature's inhomogeneity – the challenge to infer spatio-temporal dependences from paleoclimate data » publications

   1. K. Rehfeld, N. Marwan, J. Heitzig and J. Kurths: Comparison of correlation analysis techniques for irregularly sampled time series, Nonlinear Processes in Geophysics, 18(3), 389–404 (2011). DOI:10.5194/npg-18-389-2011
   2. K. Rehfeld, N. Marwan, S. F. M. Breitenbach and J. Kurths: Late Holocene Asian summer monsoon dynamics from small but complex networks of paleoclimate data, Climate Dynamics, 41(1), 3–19 (2013). DOI:10.1007/s00382-012-1448-3
   3. L. Tupikina, K. Rehfeld, N. Molkenthin, V. Stolbova, N. Marwan and J. Kurths: Characterizing the evolution of climate networks, Nonlinear Processes in Geophysics, 21, 705–711 (2014). DOI:10.5194/npg-21-705-2014

7. Jakob Runge, PhD, Humboldt University Berlin, Physics, 2011-2014 (co-supervision)
   Detecting and quantifying causality from time series of complex systems – how information theory can help in discovering interaction mechanisms in the climate system » publications

   1. J. Runge, J. Heitzig, N. Marwan and J. Kurths: Quantifying causal coupling strength: A lag-specific measure for multivariate time series related to transfer entropy, Physical Review E, 86, 061121 (2012). DOI:10.1103/PhysRevE.86.061121
   2. J. Hlinka, D. Hartman, M. Vejmelka, J. Runge, N. Marwan, J. Kurths and M. Paluš: Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information, Entropy, 15(6), 2023–2045 (2013). DOI:10.3390/e15062023
   3. J. Runge, V. Petoukhov, J. F. Donges, J. Hlinka, N. Jajcay, M. Vejmelka, D. Hartman, N. Marwan, M. Paluš and J. Kurths: Identifying causal gateways and mediators in complex spatio-temporal systems, Nature Communications, 6, 8502 (2015). DOI:10.1038/ncomms9502

8. Nora Molkenthin, PhD, University of Potsdam, Physics, 2011-2014 (co-supervision)
   Advection-diffusion-networks – on the relationship of flow dynamics and climate network topology » publications

   1. N. Molkenthin, K. Rehfeld, N. Marwan and J. Kurths: Networks from Flows – From Dynamics to Topology, Scientific Reports, 4(4119), 1–5 (2014). DOI:10.1038/srep04119
   2. L. Tupikina, K. Rehfeld, N. Molkenthin, V. Stolbova, N. Marwan and J. Kurths: Characterizing the evolution of climate networks, Nonlinear Processes in Geophysics, 21, 705–711 (2014). DOI:10.5194/npg-21-705-2014
   3. L. Tupikina, N. Molkenthin, C. López, E. Hernández-García, N. Marwan and J. Kurths: Correlation Networks from Flows. The Case of Forced and Time-Dependent Advection-Diffusion Dynamics, PLoS ONE, 11(4), e0153703 (2016). DOI:10.1371/journal.pone.0153703
   4. N. Molkenthin, H. Kutza, L. Tupikina, N. Marwan, J. F. Donges, U. Feudel, J. Kurths and R. V. Donner: Edge anisotropy and the geometric perspective on flow networks, Chaos, 27, 035802 (2017). DOI:10.1063/1.4971785

9. Bedartha Goswami, PhD, University of Potsdam, Physics, 2011-2014 (co-supervision)
   Analysis of the dynamics of palaeo and modern climate data under consideration of dating errors focussed on climate transitions and interrelations between teleconnections and regional climate » publications

   1. B. Goswami, N. Marwan, G. Feulner and J. Kurths: How do global temperature drivers influence each other? – A network perspective using recurrences, European Physical Journal – Special Topics, 222, 861–873 (2013). DOI:10.1140/epjst/e2013-01889-8
   2. B. Goswami, J. Heitzig, K. Rehfeld, N. Marwan, A. Anoop, S. Prasad and J. Kurths: Estimation of sedimentary proxy records together with associated uncertainty, Nonlinear Processes in Geophysics, 21, 1093–1111 (2014). DOI:10.5194/npg-21-1093-2014
   3. F. A. Lechleitner, J. U. L. Baldini, S. F. M. Breitenbach, J. Fohlmeister, C. McIntyre, B. Goswami, R. A. Jamieson, T. S. van der Voort, K. Prufer, N. Marwan, B. J. Culleton, D. J. Kennett, Y. Asmerom, V. Polyak and T. I. Eglinton: Hydrological and climatological controls on radiocarbon concentrations in a tropical stalagmite, Geochimica et Cosmochimica Acta, 194, 233–252 (2016). DOI:10.1016/j.gca.2016.08.039

10. Aljoscha Rheinwalt, PhD, Humboldt University Berlin, Physics, 2012-2015 (co-supervision)
    Boundary effects in spatial networks » publications

    1. A. Rheinwalt, B. Goswami, N. Boers, J. Heitzig, N. Marwan, R. Krishnan and J. Kurths: Teleconnections in Climate Networks: A Network-of-Networks Approach to Investigate the Influence of Sea Surface Temperature Variability on Monsoon Systems, In: Machine Learning and Data Mining Approaches to Climate Science, Eds.: V. Lakshmanan and E. Gilleland and A. McGovern and M. Tingley, Springer, 23–33 (2015). DOI:10.1007/978-3-319-17220-0_3
    2. A. Rheinwalt, N. Boers, N. Marwan, J. Kurths, P. Hoffmann, F.-W. Gerstengarbe and P. Werner: Non-linear time series analysis of precipitation events using regional climate networks for Germany, Climate Dynamics, 46(3), 1066–1074 (2016). DOI:10.1007/s00382-015-2632-z

11. S. B., PhD, University of Potsdam, 2011-(cancelled) (co-supervision)
    Nichtlineare Analyse hyperspektraler Fernerkundungsdaten zur Quantifizierung klimagesteuerter Erosionsprozesse

12. K. O., PhD, University of Potsdam, Geosciences, cancelled (co-supervision)
    Climate network analysis of Africa precipitation

13. Stephan Spiegel, PhD, TU Berlin, Informatics, 2012-2015 (co-supervision)
    Time Series Distance Measures: Segmentation, Classification, and Clustering of Temporal Data » publications

    1. S. Spiegel, D. Schultz and N. Marwan: Approximate Recurrence Quantification Analysis (aRQA) in Code of Best Practice, In: Recurrence Plots and Their Quantifications: Expanding Horizons, Eds.: C. L. Webber, Jr. and C. Ioana and N. Marwan, Springer, 113–136 (2016). DOI:10.1007/978-3-319-29922-8_6
    2. S. Spiegel and N. Marwan: Time and Again: Time Series Mining via Recurrence Quantification Analysis, Lecture Notes in Computer Science, 9853, 258–262 (2016). DOI:10.1007/978-3-319-46131-1_30
    3. D. Schultz, S. Spiegel, N. Marwan and S. Albayrak: Approximation of diagonal line based measures in recurrence quantification analysis, Physics Letters A, 379(14–15), 997–1011 (2015). DOI:10.1016/j.physleta.2015.01.033

14. Deniz Eroglu, PhD, Humboldt University Berlin, Physics, 2013-2016 (co-supervision)
    Reconstruction of palaeo-climatic conditions across the Sunda Shelf in the Late Pleistocene by analysis of stable isotopes of stalagmites » publications

    1. D. Eroglu, T. K. D. Peron, N. Marwan, F. A. Rodrigues, L. d. F. Costa, M. Sebek, I. Z. Kiss and J. Kurths: Entropy of weighted recurrence plots, Physical Review E, 90, 042919 (2014). DOI:10.1103/PhysRevE.90.042919
    2. D. Eroglu, N. Marwan, S. Prasad and J. Kurths: Finding recurrence networks' threshold adaptively for a specific time series, Nonlinear Processes in Geophysics, 21, 1085–1092 (2014). DOI:10.5194/npg-21-1085-2014
    3. I. Ozken, D. Eroglu, T. Stemler, N. Marwan, G. B. Bagci and J. Kurths: Transformation-cost time-series method for analyzing irregularly sampled data, Physical Review E, 91, 062911 (2015). DOI:10.1103/PhysRevE.91.062911
    4. O. Afsar, D. Eroglu, N. Marwan and J. Kurths: Scaling behaviour for recurrence-based measures at the edge of chaos, Europhysics Letters, 112(1), 10005 (2015). DOI:10.1209/0295-5075/112/10005
    5. D. Eroglu, F. H. McRobie, I. Ozken, T. Stemler, K.-H. Wyrwoll, S. F. M. Breitenbach, N. Marwan and J. Kurths: See-saw relationship of the Holocene East Asian-Australian summer monsoon, Nature Communications, 7, 12929 (2016). DOI:10.1038/ncomms12929
    6. I. Ozken, D. Eroglu, S. F. M. Breitenbach, N. Marwan, L. Tan, U. Tirnakli and J. Kurths: Recurrence plot analysis of irregularly sampled data, Physical Review E, 98, 052215 (2018). DOI:10.1103/PhysRevE.98.052215
    7. N. Marwan, D. Eroglu, I. Ozken, T. Stemler, K.-H. Wyrwoll and J. Kurths: Regime Change Detection in Irregularly Sampled Time Series, In: Advances in Nonlinear Geosciences, Eds.: A. A. Tsonis, Springer International, 357–368 (2018). DOI:10.1007/978-3-319-58895-7_18
    8. Y. Hirata, T. Stemler, D. Eroglu and N. Marwan: Prediction of flow dynamics using point processes, Chaos, 28, 011101 (2018). DOI:10.1063/1.5016219
    9. D. Eroglu, N. Marwan, M. Stebich and J. Kurths: Multiplex recurrence networks, Physical Review E, 97, 012312 (2018). DOI:10.1103/PhysRevE.97.012312
    10. S. Prasad, N. Marwan, D. Eroglu, B. Goswami, P. K. Mishra, B. Gaye, A. Anoop, N. Basavaiah, M. Stebich and A. Jehangir: Holocene climate forcings and lacustrine regime shifts in the Indian summer monsoon realm, Earth Surface Processes and Landforms, 45(15), 3842–3853 (2020). DOI:10.1002/esp.5004
    11. N. Marwan, J. F. Donges, R. V. Donner and D. Eroglu: Nonlinear time series analysis of palaeoclimate proxy records, Quaternary Science Reviews, 274, 107245 (2021). DOI:10.1016/j.quascirev.2021.107245

15. Peng Ji, PhD, Humboldt University Berlin, Physics, 2013-2015 (co-supervision)
    Synchronisation in the Second-order Kuramoto Model

16. Tobias Rawald, PhD, Humboldt University Berlin, Informatics, 2014-2017 (co-supervision)
    Scalable and Efficient Analysis of Large High-Dimensional Data Sets in the Context of Recurrence Analysis » publications

    1. T. Rawald, M. Sips, N. Marwan and D. Dransch: Fast Computation of Recurrences in Long Time Series, In: Translational Recurrences – From Mathematical Theory to Real-World Applications, Eds.: N. Marwan and M. A. Riley and A. Giuliani and C. L. Webber, Jr., Springer, 17–29 (2014). DOI:10.1007/978-3-319-09531-8_2
    2. T. Rawald, M. Sips, N. Marwan and U. Leser: Massively Parallel Analysis of Similarity Matrices on Heterogeneous Hardware, Proceedings of the EDBT/ICDT Joint Conference 2015, 56–62 (2015). http://ceur-ws.org/Vol-1330/paper-11
    3. M. Sips, C. Witt, T. Rawald and N. Marwan: Torwards Visual Analytics for the Exploration of Large Sets of Time Series, In: Recurrence Plots and Their Quantifications: Expanding Horizons, Eds.: C. L. Webber, Jr. and C. Ioana and N. Marwan, Springer, 3–17 (2016). DOI:10.1007/978-3-319-29922-8_1
    4. T. Rawald, M. Sips and N. Marwan: PyRQA – Conducting Recurrence Quantification Analysis on Very Long Time Series Efficiently, Computers & Geosciences, 104, 101–108 (2017). DOI:10.1016/j.cageo.2016.11.016

17. F. B., PhD, University of Potsdam, Geosciences, 2015-(cancelled)
    InfectControl2020 » publications

    1. F. Brenner, N. Marwan and P. Hoffmann: Climate impact on spreading of airborne infectious diseases, European Physical Journal – Special Topics, 226(9), 1845–1856 (2017). DOI:10.1140/epjst/e2017-70028-2
    2. F. Brenner and N. Marwan: Change of influenza pandemics because of climate change: Complex network simulations, Revue d'Épidémiologie et de Santé Publique, 66(Suppl. 5), S424 (2018). DOI:10.1016/j.respe.2018.05.513

18. Ankit Agarwal, PhD, University of Potsdam, Environmental sciences, 2015-2019
    Unraveling spatio-temporal climatic patterns via multi-scale complex networks » publications

    1. A. Agarwal, N. Marwan, M. Rathinasamy, B. Merz and J. Kurths: Multi-scale event synchronization analysis for unravelling climate processes: A wavelet-based approach, Nonlinear Processes in Geophysics, 24, 599–611 (2017). DOI:10.5194/npg-24-599-2017
    2. U. Ozturk, N. Marwan, O. Korup, H. Saito, A. Agarwal, M. J. Grossman, M. Zaiki and J. Kurths: Complex networks for tracking extreme rainfall during typhoons, Chaos, 28(7), 075301 (2018). DOI:10.1063/1.5004480
    3. A. Agarwal, N. Marwan, R. Maheswaran, B. Merz and J. Kurths: Quantifying the roles of single stations within homogeneous regions using complex network analysis, Journal of Hydrology, 563, 802–810 (2018). DOI:10.1016/j.jhydrol.2018.06.050
    4. A. Agarwal, N. Marwan, U. Ozturk and R. Maheswaran: Unfolding Community Structure in Rainfall Network of Germany Using Complex Network-Based Approach, In: Water Resources and Environmental Engineering II, Springer, 179–193 (2018). DOI:10.1007/978-981-13-2038-5_17
    5. A. Agarwal, R. Maheswaran, N. Marwan, L. Caesar and J. Kurths: Wavelet-based multiscale similarity measure for complex networks, European Physical Journal B, 91(11), 296 (2018). DOI:10.1140/epjb/e2018-90460-6
    6. M. Rathinasamy, A. Agarwal, B. Sivakumar, N. Marwan and J. Kurths: Wavelet analysis of precipitation extremes over India and teleconnections to climate indices, Stochastic Environmental Research and Risk Assessment, 33(11–12), 2053–2069 (2019). DOI:10.1007/s00477-019-01738-3
    7. J. Kurths, A. Agarwal, R. Shukla, N. Marwan, M. Rathinasamy, L. Caesar, R. Krishnan and B. Merz: Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach, Nonlinear Processes in Geophysics, 26(3), 251–266 (2019). DOI:10.5194/npg-26-251-2019
    8. N. Ekhtiari, A. Agarwal, N. Marwan and R. V. Donner: Disentangling the multi-scale effects of sea-surface temperatures on global precipitation: A coupled networks approach, Chaos, 29, 063116 (2019). DOI:10.1063/1.5095565
    9. A. Agarwal, L. Caesar, N. Marwan, R. Maheswaran, B. Merz and J. Kurths: Network-based identification and characterization of teleconnections on different scales, Scientific Reports, 9, 8808 (2019). DOI:10.1038/s41598-019-45423-5
    10. R. Kumar Guntu and P. Kumar Yeditha, M. Rathinasamy, M. Perc, N. Marwan, J. Kurths and A. Agarwal: Wavelet entropy-based evaluation of intrinsic predictability of time series, Chaos, 30, 033117 (2020). DOI:10.1063/1.5145005
    11. A. Agarwal, N. Marwan, R. Maheswaran, U. Ozturk, J. Kurths and B. Merz: Optimal design of hydrometric station networks based on complex network analysis, Hydrology and Earth System Sciences, 24(), 2235–2251 (2020). DOI:10.5194/hess-24-2235-2020

19. Dadiyorto Wendi, PhD, University of Potsdam, Environmental sciences, 2015-2019
    Recurrence plots and quantification of flood runoff dynamics » publications

    1. D. Wendi, N. Marwan and B. Merz: In search of determinism-sensitive region to avoid artefacts in recurrence plots, International Journal of Bifurcation and Chaos, 28(1), 1850007 (2018). DOI:10.1142/S0218127418500074
    2. D. Wendi and N. Marwan: Extended recurrence plot and quantification for noisy continuous dynamical systems, Chaos, 28(8), 085722 (2018). DOI:10.1063/1.5025485
    3. D. Wendi, B. Merz and N. Marwan: Assessing Hydrograph Similarity and Rare Runoff Dynamics by Cross Recurrence Plots, Water Resources Research, 55(6), 4704–4726 (2019). DOI:10.1029/2018WR024111
    4. D. Wendi, B. Merz and N. Marwan: Novel Quantification Method for Hydrograph Similarity, In: Advances in Hydroinformatics. Springer Water, Eds.: P. Gourbesville and G. Caignaert, Springer, 727–734 (2020). DOI:10.1007/978-981-15-5436-0_56

20. Ugur Öztürk, PhD, University of Potsdam, Environmental sciences, 2015-2019
    Learning more to predict landslides » publications

    1. U. Ozturk, N. Marwan, S. Specht, O. Korup and J. Jensen: A new centennial sea-level record for Antalya, eastern Mediterranean, Journal of Geophysical Research: Ocean, 123(7), 4503–4517 (2018). DOI:10.1029/2018JC013906
    2. U. Ozturk, N. Marwan, O. Korup, H. Saito, A. Agarwal, M. J. Grossman, M. Zaiki and J. Kurths: Complex networks for tracking extreme rainfall during typhoons, Chaos, 28(7), 075301 (2018). DOI:10.1063/1.5004480
    3. A. Agarwal, N. Marwan, U. Ozturk and R. Maheswaran: Unfolding Community Structure in Rainfall Network of Germany Using Complex Network-Based Approach, In: Water Resources and Environmental Engineering II, Springer, 179–193 (2018). DOI:10.1007/978-981-13-2038-5_17
    4. U. Ozturk, N. Malik, K. Cheung, N. Marwan and J. Kurths: A network-based comparative study of extreme tropical and frontal storm rainfall over Japan, Climate Dynamics, 53(1–2), 521–532 (2019). DOI:10.1007/s00382-018-4597-1
    5. A. Agarwal, N. Marwan, R. Maheswaran, U. Ozturk, J. Kurths and B. Merz: Optimal design of hydrometric station networks based on complex network analysis, Hydrology and Earth System Sciences, 24(), 2235–2251 (2020). DOI:10.5194/hess-24-2235-2020

21. Hauke Krämer, PhD, University of Potsdam, Physics, 2017–2021
    Towards a robust framework for recurrence analysis » publications

    1. K. H. Kraemer, R. V. Donner, J. Heitzig and N. Marwan: Recurrence threshold selection for obtaining robust recurrence characteristics in different embedding dimensions, Chaos, 28(8), 085720 (2018). DOI:10.1063/1.5024914
    2. K. H. Kraemer and N. Marwan: Border effect corrections for diagonal line based recurrence quantification analysis measures, Physics Letters A, 383(34), 125977 (2019). DOI:10.1016/j.physleta.2019.125977
    3. M. H. Trauth, A. Asrat, W. Duesing, V. Foerster, K. H. Kraemer, N. Marwan, M. A. Maslin and F. Schäbitz: Classifying past climate change in the Chew Bahir basin, southern Ethiopia, using recurrence quantification analysis, Climate Dynamics, 53(5), 2557–2572 (2019). DOI:10.1007/s00382-019-04641-3
    4. K. H. Kraemer, G. Datseris, J. Kurths, I. Z. Kiss, J. L. Ocampo-Espindola and N. Marwan: A unified and automated approach to attractor reconstruction, New Journal of Physics, 23, 033017 (2021). DOI:10.1088/1367-2630/abe336
    5. W. Duesing, N. Berner, A. L. Deino, V. Foerster, K. H. Kraemer, N. Marwan and M. H. Trauth: Multiband Wavelet Age Modeling for a \~293 m (\~600 kyr) Sediment Core From Chew Bahir Basin, Southern Ethiopian Rift, Frontiers in Earth Sciences, 9, 594047 (2021). DOI:10.3389/feart.2021.594047
    6. M. H. Trauth, A. Asrat, A. S. Cohen, W. Duesing, V. Foerster, S. Kaboth-Bahr, K. H. Kraemer, H. F. Lamb, N. Marwan, M. A. Maslin and F. Schäbitz: Recurring types of variability and transitions in the $\sim620$ kyr record of climate change from the Chew Bahir basin, southern Ethiopia, Quaternary Science Reviews, 266, 106777 (2021). DOI:10.1016/j.quascirev.2020.106777
    7. K. H. Kraemer, M. Gelbrecht, I. Pavithran, R. I. Sujith and N. Marwan: Optimal state space reconstruction via Monte Carlo Decision Tree Search, Nonlinear Dynamics, 108, 1525–1545 (2022). DOI:10.1007/s11071-022-07280-2
    8. K. H. Kraemer, F. Hellmann, M. Anvari, J. Kurths and N. Marwan: Spike spectra for recurrences, Entropy, 24(11), 1689 (2022). DOI:10.3390/e24111689
    9. N. Marwan and K. H. Kraemer: Trends in recurrence analysis of dynamical systems, European Physical Journal – Special Topics, 232, 5–27 (2023). DOI:10.1140/epjs/s11734-022-00739-8
    10. J. M. Dhadphale, K. H. Kraemer, M. Gelbrecht, J. Kurths, N. Marwan and R. I. Sujith: Model adaptive phase space reconstruction, Chaos, 34, 073125 (2024). DOI:10.1063/5.0194330

22. Walter Düsing, PhD, University of Potsdam, Geosciences, 2017–2020
    Trends, rhythms and events in East African climate: statistical analysis of the paleoclimare records of the long sediment cores of the Chew Bahir basin » publications

    1. M. H. Trauth, A. Asrat, W. Duesing, V. Foerster, K. H. Kraemer, N. Marwan, M. A. Maslin and F. Schäbitz: Classifying past climate change in the Chew Bahir basin, southern Ethiopia, using recurrence quantification analysis, Climate Dynamics, 53(5), 2557–2572 (2019). DOI:10.1007/s00382-019-04641-3
    2. W. Duesing, N. Berner, A. L. Deino, V. Foerster, K. H. Kraemer, N. Marwan and M. H. Trauth: Multiband Wavelet Age Modeling for a \~293 m (\~600 kyr) Sediment Core From Chew Bahir Basin, Southern Ethiopian Rift, Frontiers in Earth Sciences, 9, 594047 (2021). DOI:10.3389/feart.2021.594047
    3. M. H. Trauth, A. Asrat, A. S. Cohen, W. Duesing, V. Foerster, S. Kaboth-Bahr, K. H. Kraemer, H. F. Lamb, N. Marwan, M. A. Maslin and F. Schäbitz: Recurring types of variability and transitions in the $\sim620$ kyr record of climate change from the Chew Bahir basin, southern Ethiopia, Quaternary Science Reviews, 266, 106777 (2021). DOI:10.1016/j.quascirev.2020.106777

23. Matthias Kemter, PhD, University of Potsdam, Environmental sciences, 2018-2022
    Investigation of interrelationships between floods and climate variability using multi-layer complex networks » publications

    1. M. Kemter, B. Merz, N. Marwan, S. Vorogushyn and G. Blöschl: Joint Trends in Flood Magnitudes and Spatial Extents Across Europe, Geophysical Research Letters, 47(7), e2020GL087464 (2020). DOI:10.1029/2020GL087464
    2. M. Kemter, N. Marwan, G. Villarini and B. Merz: Controls on Flood Trends Across the United States, Water Resources Research, 59(2), e2021WR031673 (2023). DOI:10.1029/2021WR031673
    3. A. Banerjee, M. Kemter, B. Goswami, B. Merz, J. Kurths and N. Marwan: Spatial coherence patterns of extreme winter precipitation in the U.S., Theoretical and Applied Climatology, 152, 385–395 (2023). DOI:10.1007/s00704-023-04393-5

24. Abhirup Banerjee, PhD, University of Potsdam, Physics, 2018-2022
    Characterizing the spatio-temporal patterns of extreme events » publications

    1. A. Banerjee, B. Goswami, Y. Hirata, D. Eroglu, B. Merz, J. Kurths and N. Marwan: Recurrence analysis of extreme event-like data, Nonlinear Processes in Geophysics, 28, 213–229 (2021). DOI:10.5194/npg-28-213-2021
    2. A. Banerjee, A. Mishra, S. K. Dana, C. Hens, T. Kapitaniak, J. Kurths and N. Marwan: Predicting the data structure prior to extreme events from passive observables using echo state network, Frontiers in Applied Mathematics and Statistics, 8, 955044 (2022). DOI:10.3389/fams.2022.955044
    3. A. Agarwal, R. K. Guntu, A. Banerjee, M. A. Gadhawe and N. Marwan: A complex network approach to study the extreme precipitation patterns in a river basin, Chaos, 32, 013113 (2022). DOI:10.1063/5.0072520
    4. A. Banerjee, M. Kemter, B. Goswami, B. Merz, J. Kurths and N. Marwan: Spatial coherence patterns of extreme winter precipitation in the U.S., Theoretical and Applied Climatology, 152, 385–395 (2023). DOI:10.1007/s00704-023-04393-5
    5. S. Gupta, A. Banerjee, N. Marwan, D. Richardson, L. Magnusson, J. Kurths and F. Pappenberger: Analysis of Spatially Coherent Forecast Error Structures, Quaterly Journal of the Royal Meteorological Socicety, 149(756), 2881–2894 (2023). DOI:10.1002/qj.4536

25. K. M., PhD, University of Potsdam, Environmental sciences, 2018–2019(cancelled)
    Learning landslide triggers from complex networks

26. Lisa Luna, PhD, University of Potsdam, Environmental sciences, 2019–2023
    Learning landslide triggers from complex networks

27. Tobias Braun, PhD, University of Potsdam, Physics, 2019–2022
    Recurrences in past climates » publications

    1. T. Braun, V. R. Unni, R. I. Sujith, J. Kurths and N. Marwan: Detection of dynamical regime transitions with lacunarity as a multiscale recurrence quantification measure, Nonlinear Dynamics, 104, 3955–3973 (2021). DOI:10.1007/s11071-021-06457-5
    2. O. Kwiecien, T. Braun, C. F. Brunello, P. Faulkner, N. Hausmann, G. Helle, J. A. Hoggarth, M. Ionita, C. Jazwa, S. Kelmelis, N. Marwan, C. Nava-Fernandez, C. Nehme, T. Opel, J. L. Oster, A. Perşoiu, C. Petrie, K. Prufer, S. M. Saarni, A. Wolf and S. F. M. Breitenbach: What we talk about when we talk about seasonality – A transdisciplinary review, Earth-Science Reviews, 225, 103843 (2022). DOI:10.1016/j.earscirev.2021.103843
    3. T. Braun, C. N. Fernandez, D. Eroglu, A. Hartland, S. F. M. Breitenbach and N. Marwan: Sampling rate-corrected analysis of irregularly sampled time series, Physical Review E, 105, 024206 (2022). DOI:10.1103/PhysRevE.105.024206
    4. D. Mukhin, A. Hannachi, T. Braun and N. Marwan: Revealing recurrent regimes of mid-latitude atmospheric variability using novel machine learning method, Chaos, 32(11), 113105 (2022). DOI:10.1063/5.0109889
    5. N. Marwan and T. Braun: Power spectral estimate for discrete data, Chaos, 33(5), 053118 (2023). DOI:10.1063/5.0143224
    6. T. Braun, K. H. Kraemer and N. Marwan: Recurrence flow measure of nonlinear dependence, European Physical Journal – Special Topics, 232, 57–67 (2023). DOI:10.1140/epjs/s11734-022-00687-3
    7. T. Braun, S. F. M. Breitenbach, V. Skiba, F. A. Lechleitner, E. E. Ray, L. M. Baldini, V. J. Polyak, J. U. L. Baldini, D. J. Kennett, K. M. Prufer and N. Marwan: Decline in seasonal predictability potentially destabilized Classic Maya societies, Communications Earth & Environment, 4, 82 (2023). DOI:10.1038/s43247-023-00717-5
    8. T. Haselhoff, T. Braun, A. Fiebig, J. Hornberg, B. T. Lawrence, N. Marwan and S. Moebus: Complex networks for analyzing the urban acoustic environment, Ecological Informatics, 78, 102326 (2023). DOI:10.1016/j.ecoinf.2023.102326
    9. C. Nava-Fernandez, T. Braun, C. L. Pederson, B. Fox, A. Hartland, O. Kwiecien, S. N. Höpker, S. Bernasconi, M. Jaggi, J. Hellstrom, F. Gázquez, A. French, N. Marwan, A. Immenhauser and S. F. M. Breitenbach: Mid-Holocene rainfall seasonality and ENSO dynamics over the south-western Pacific, The Depositional Record, 10(1), 176–194 (2024). DOI:10.1002/dep2.268

28. Keno Riechers, PhD, Freie Universität Berlin, 2019– (co-supervision)
    Spatio-temporal interaction patterns between tipping elements and possible tipping cascades

29. Vanessa Skiba, PhD, University of Potsdam, Geosciences, 2020–2024
    Alpine speleothems as recorder of glacier evolution » publications

    1. V. Skiba, C. Spötl, M. Trüssel, A. Schröder-Ritzrau, B. Schröder, N. Frank, R. Eichstädter, R. Tjallingii, N. Marwan, X. Zhang and J. Fohlmeister: Millennial-scale climate variability in the Northern Hemisphere influenced glacier dynamics in the Alps around 250,000 years ago, Communications Earth & Environment, 4, 426 (2023). DOI:10.1038/s43247-023-01083-y
    2. V. Skiba, G. Jouvet, N. Marwan, C. Spötl and J. Fohlmeister: Speleothem growth and stable carbon isotopes as proxies of the presence and thermodynamical state of glaciers compared to modelled glacier evolution in the Alps, Quaternary Science Reviews, 322, 108403 (2023). DOI:10.1016/j.quascirev.2023.108403

30. Sara Bernal, PhD, University of Potsdam, Geosciences, 2020–2025
    BMBF-Projekt climXtreme: Spatial synchronization patterns of heavy precipitation events in Europe » publications

    1. S. M. Vallejo-Bernal, F. Wolf, N. Boers, D. Traxl, N. Marwan and J. Kurths: The role of atmospheric rivers in the distribution of heavy precipitation events over North America, Hydrology and Earth System Sciences, 27(4), 2645–2660 (2023). DOI:10.5194/hess-27-2645-2023
    2. G. Chopra, V. R. Unni, P. Venkatesan, S. M. Vallejo-Bernal, N. Marwan, J. Kurths and R. I. Sujith: Community structure of tropics emerging from spatio-temporal variations in the Intertropical Convergence Zone dynamics, Scientific Reports, 14, 24463 (2024). DOI:10.1038/s41598-024-73872-0
    3. S. M. Vallejo-Bernal, T. Braun, N. Marwan and J. Kurths: PIKART: A Comprehensive Global Catalog of Atmospheric Rivers, Journal of Geophysical Research: Atmospheres, 130(15), e2024JD041869 (2025). DOI:10.1029/2024JD041869

31. Jonas Wassmer, PhD, University of Potsdam, Physics, 2021–2026
    Impact of extreme events on topological robustness of interdependent networks » publications

    1. J. Wassmer, B. Merz and N. Marwan: Resilience of transportation infrastructure networks to road failures, Chaos, 34, 013124 (2024). DOI:10.1063/5.0165839
    2. J. Wassmer, S. Bryant, P. Schimansky, L. T. Keegan, M. Pregnolato, J. Kurths, N. Marwan and B. Merz: Unveiling hidden risks in healthcare from flood-induced transportation disruption in Germany, Communications Earth & Environment, 6, 676 (2025). DOI:10.1038/s43247-025-02645-y
    3. J. Wassmer, N. Antary, C. Hartmann and N. Marwan: A social cost gradient centrality for transportation networks, European Physical Journal - Special Topics, () (in press). DOI:10.1140/epjs/s11734-026-02291-1

32. Joaquin Ferrer, PhD, University of Potsdam, Environmental sciences, 2021–2024
    Exploring Global Exposure to Large Landslides » publications

    1. J. V. Ferrer, G. S. Mohor, O. Dewitte, T. Pánek, C. Reyes-Carmona, A. L. Handwerger, M. Hürlimann, L. Köhler, K. Teshebaeva, A. H. Thieken, C.-Y. Tsou, A. U. Vinueza, V. Demurtas, Y. Zhang, C. Zhao, N. Marwan, J. Kurths and O. Korup: Human Settlement Pressure Drives Slow-Moving Landslide Exposure, Earth's Future, 12(9), e2024EF004830 (2024). DOI:10.1029/2024EF004830
    2. J. V. Ferrer, C. B. Moroz, S. Yüksel, O. Dewitte, K. Lebek, N. Marwan, J. Kurths and O. Korup: Exposure to Large Landslides in Cities Outpaces Urban Growth, Geophysical Research Letters, 52(15), e2025GL115170 (2025). DOI:10.1029/2025GL115170

33. Jianxin Zhang, PhD, Beijing Normal University, 2023–2025 (co-supervision)
    » publications

    1. J. Zhang, K. Liu, M. Wang, K. Li, F., J. Ludescher, J. Kurths and N. Marwan: High predictability potential of highly synchronized widespread floods in monsoon regions, Journal of Hydrology, 668, 135006 (2026). DOI:10.1016/j.jhydrol.2026.135006
    2. J. Zhang, K. Liu, Y. Sui, K. Li, L. Qin, J. Liu, M. Wang and N. Marwan: Oscillation-induced synchronization hubs in global hydrological extremes, Journal of Hydrology, 674, 135550 (2026). DOI:10.1016/j.jhydrol.2026.135550

34. Maria Mannone, PhD, University of Potsdam, Physics, 2023–2026
    Unraveling Brain Network Organisation through Connectome Analysis – An application to Pathological Dynamics » publications

    1. M. Mannone, N. Marwan, V. Seidita, A. Chella, A. Giacometti and P. Fazio: Entangled Gondolas. Design of Multi-layer Networks of Quantum-Driven Robotic Swarms, In: Artificial Life and Evolutionary Computation. WIVACE 2023, Springer Nature Switzerland, 177–189 (2024). DOI:10.1007/978-3-031-57430-6_14
    2. M. Mannone, P. Fazio, N. Marwan and A. Giacometti: Multi-Layer Networks and Routing Protocols for Aquatic Robotic Swarm Management, Advances in Electrical and Electronic Engineering, 22(2), 193–201 (2024). DOI:10.15598/aeee.v22i2.5595
    3. M. Mannone, P. Fazio and N. Marwan: Modeling a neurological disorder as the result of an operator acting on the brain: A first sketch based on network channel modeling, Chaos, 34, 053133 (2024). DOI:10.1063/5.0199988
    4. M. Mannone, P. Fazio, P. Ribino and N. Marwan: On disease and healing: a theoretical sketch, Frontiers in Applied Mathematics and Statistics, 10, 1468556 (2024). DOI:10.3389/fams.2024.1468556
    5. S. Fazio, P. Ribino, F. Gasparini, N. Marwan, P. Fazio, M. Gherardi and M. Mannone: A Physics-Based View of Brain-Network Alteration in Neurological Disease, EasyChair Preprint, 15483 (2024). https://easychair.org/publications/preprint/HrLJ
    6. M. Mannone, P. Fazio, J. Kurths, P. Ribino and N. Marwan: A brain-network operator for modeling disease: a first data-based application for Parkinson's disease, European Physical Journal – Special Topics, 234, 119–140 (2025). DOI:10.1140/epjs/s11734-024-01345-6
    7. M. Mannone, N. Marwan, P. Fazio and P. Ribino: Limbic and cerebellar effects in Alzheimer-Perusini’s disease: A physics-inspired approach, Biomedical Signal Processing and Control, 103, 107355 (2025). DOI:10.1016/j.bspc.2024.107355
    8. M. Mannone, P. Ribino, A. Saibene, P. Fazio, S. Fazio, F. Gasparini, M. Gherardi and N. Marwan: Computing the Time-dependent Krankheit-operator in Epilepsy from ECoG: a Case Study, 100–104 (2025). https://sensorsportal.com/ASPAI_2025/ASPAI_2025_Proceedings.pdf
    9. M. Mannone, N. Marwan, P. Fazio and P. Ribino: Broken AI. A Test Bench for a Non-invasive Experiment in Computational Neuropsychiatry Joining Krankheit-Operator and Artificial Intelligence, 105–109 (2025). https://sensorsportal.com/AIMH_2025/AIMH_2025_Proceedings.pdf
    10. M. Mannone, P. Ribino, P. Fazio and N. Marwan: Sketching a Space of Brain States, Neuroinformatics, 23, 45 (2025). DOI:10.1007/s12021-025-09739-0
    11. S. Fazio, P. Ribino, F. Gasparini, N. Marwan, P. Fazio, M. Gherardi and M. Mannone: $K$-operator for Modelling Neurodegeneration: Simulations, fMRI Application, Eigenvalue Analysis and Recurrence Plots, Journal of Medical Systems, 49, 144 (2025). DOI:10.1007/s10916-025-02244-6
    12. P. V. Itaboraí, O. C. Hamido, N. Marwan, P. Fazio, P. Ribino and M. Mannone: Quantum seizures: sonifying quantum modelling of epileptic time series, European Physical Journal – Special Topics, () (in press). DOI:10.1140/epjs/s11734-026-02341-8
    13. P. Fazio, M. Mannone, N. Marwan, P. Ribino, M. Mehi, A. Swikir, D. Amendola, P. Riello and M. Voznak: Telecommunication-inspired network models of healthy and diseased brains, Scientific Reports, 16, 19686 (2026). DOI:10.1038/s41598-026-50758-x
    14. M. Mannone and N. Marwan: Quantum-inspired density-matrix recurrence analysis of brain time series, European Physical Journal – Special Topics, () (in press). DOI:10.1140/epjs/s11734-026-02340-9

35. Nina Dörfler, PhD, University of Potsdam, Physics, 2025–
    

36. Florian Leder, PhD, University of Potsdam, Geosciences, 2025–
    

37. Zinan Lyu, PhD, Humboldt University Berlin, Geosciences/ GFZ, 2025–
    

38. Anusha Ganapathiraju, PhD, IIT Hyderabad, 2025– (co-supervision)
    » publications

    1. S. A. Ganapathiraju, U. Kanth, M. S. Latha V., M. Rathinasamy, N. K. Kondapalli, S. M. Vallejo-Bernal, N. Marwan and S. Kaur: Spatiotemporal dynamics of moisture influx and their role in precipitation extremes: A study of December 2023 in Kayalpattinam, Atmospheric Research, 331, 108680 (2026). DOI:10.1016/j.atmosres.2025.108680