Minisymposia

Call for minisymposia

The call for organizing Minisymposia is opened. Interested organizers may send their proposal to ddeu2025@auth.gr

The proposal should contain

1.  Names, affiliations and contact details of organizers.

2. Title and a short description (one paragraph) for the topic of the proposed minisymposia.

3. A tentative list of potential speakers (names and affiliations).

A minisymposium should have a total duration of 120min. It may have 4 talks of 30 min duration (25 min presentation plus 5 min for questions), but other forms may be suggested as well, e.g. 6 talks of 20 min duration (17  min presentation plus 3 min for questions). A minisymposium may have more than one two-hour session, e.g., part I and part II.

Decision for acceptance / rejection of the proposed minisymposium will be communicated within a week after the proposal is made.

Regarding the submission of the talks in the minisymposium, this is done by invitation of the minisymposia organizers. The submissions are then directed to the minisymposia organizers, who collect them and forward them to the DDE2025 organizers (by email to ddeu2025@auth.gr). The minisymposium organizers may want to suggest to their minisymposium participants to use the abstract template DDE2025 uses for the contributed talks (see Submission web-page). 

All presenters in the minisymposium should be registered (see Registration web-page).

 

List of minisymposia

The following minisymposia will take place in DDE2025. The exact dates/times will be added when the program is announced. Please note that as for now the list of speakers is tentative.

MS1 Session Title: Multistability and Nonlocal Stability Analysis

Session Organisers:
Datseris, George, University of Exeter
email: G.Datseris@exeter.ac.uk
Rossi, Kalel Luiz, University of Oldenburg,
email: kalel.luiz.rossi@uni-oldenburg.de

Several dynamical systems are multistable: they exhibit a coexistence of stable solutions, formally called attractors. Examples include power grids, climate components, the brain, mechanical and metabolic systems, to name a few. Perturbations, such as noise or external shocks, can induce transitions between these attractors – which, depending on the application, may be either desirable or catastrophic. It becomes crucial therefore to study the stability in such multistable systems. Typically stability is studied using local bifurcation analysis and continuation, but this approach can be unsuitable for real-world applications where perturbations are finite-sized instead of infinitesimal. This calls for a nonlocal view of stability. Recent progress has enriched the literature with various quantities that can be used as quantifiers of nonlocal stability: basin stability or volume, the geometry of the basins, basin entropy, return time, minimal fatal shock, and other notions of resilience. In this minisymposium we want to highlight and promote recent research that explores one or several of the following categories:

– novel indicators of nonlocal stability
– novel techniques for finding multiple system attractors and/or their basins of attraction
– nonlocal stability analysis and continuation of multistable systems
– multistability in high-dimensional systems
– very high (10+) or extreme multistability (infinitely many coexisting attractors)
– multistability in chaotic systems

George Datseris – University of Exeter.

Kalel L. Rossi – University of Oldenburg.

Andreas Morr – Potsdam Institute for Climate Impact Research.

Alex Wagemakers – Universidad Rey Juan Carlos.

Muhammed Fadera – University of Exeter.

Arturo C. Marti – Universidad de la Republica.

Dawid Dudkowski – Lodz University of Technology.

Yuanzhao Zhang – Santa Fe Institute.

Andrew Flynn – University College Cork.

Manish Dev Shrimali – Central University of Rajasthan.

MS2 Session Title: Advances in Theoretical and Practical Applications for Infectious Diseases and Control

Session Organisers:
Steindorf, Vanessa, Basque Center for Applied Mathematics, Bilbao, Spain
email: vsteindorf@bcamath.org
Aguiar, Maíra, Basque Center for Applied Mathematics, Bilbao, Spain
email: maguiar@bcamath.org

Focused on future research directions for modeling the spread of pathogens capable of causing new outbreaks, this interdisciplinary symposium aims to promote timely debates exploring various approaches in epidemiology, particularly on the mathematical modeling of infectious respiratory and vector-borne diseases. Key discussions will cover recent advances in mathematical epidemiology, offering a comprehensive look at both theoretical methods and practical applications. Topics will include the role of temporal and spatial dynamics in disease transmission, the challenges of predicting epidemic trends, improving predictive models to inform public health strategies, and the integration of environmental and human behavior factors into models. By bridging theory and practice, the symposium aims to enhance tools for disease control and outbreak preparedness.

1. Vanessa Steindorf
Basque Center for Applied Mathematics, Bilbao, Spain
vsteindorf@bcamath.org
2. Maíra Aguiar
Basque Center for Applied Mathematics, Bilbao, Spain
maguiar@bcamath.org
3. Nico Stollenwerk
Basque Center for Applied Mathematics, Bilbao, Spain
nstollenwerk@bcamath.org
4. Thomas Goetz
University of Koblenz, Koblenz, Germany
goetz@uni-koblenz.de
5. Paula Patrcio
Center for Mathematics and Applications (NOVA Math) and
Department of Mathematics, NOVA FCT, Lisbon, Portugal
pcpr@fct.unl.pt
6. Carlo Estadilla
Bristol Medical School
University of Bristol, Bristol, UK
carlo.estadilla@gmail.com
7. Chiara Cicolani
Universit degli studi dell’ Aquila, L’ Aquilla, Italy
Basque Center for Applied Mathematics, Bilbao, Spain
chiara.cicolani@graduate.univaq.it
8. Akhil K. Srivastav
Basque Center for Applied Mathematics, Bilbao, Spain
asrivastav@bcamath.org

MS3 Session Title: Quantum chaos in few and many body systems

Session Organiser:
Robnik, Marko, CAMTP – Center for Applied Mathematics and Theoretical Physics, University of Maribor, Maribor, Slovenia
email: marko.robnik@guest.um.si

The talks will cover the topics on most recent results in quantum chaos of few and many body systems. We shall address the semiclassical behavior of quantum systems, and their correspondence to the associated classical chaotic systems, as well as the studies of quantum systems without a classical analog. In particular, we shall focus on the following aspects: spectral statistics, phase space localization of eigenstates, the statistical properties of the localization measures, ergodicity properties and their hierarchy, dissipative quantum systems, localization and thermalization, time evolution, in particular studies of OTOC (out-of-time order correlators). Several model systems will be presented and analyzed, on which the phenomenological results are based, as well as they are a basis for theoretical approaches.

Part I (120 min):

1. Giulio Casati, University of Insubria, Como, Italy (30 min)
2. Marko Robnik, CAMTP, University of Maribor, Slovenia (30 min)
3. Thomas Guhr, University of Duisburg-Essen, Germany (30 min)
4. Juan Diego Urbina, University of Regensburg, Germany (30 min)

Part II (110 min):

1. Qian Wang, CAMTP, University of Maribor and Zhejiang Normal University, Jinhua, China (30 min)
2. Hua Yan, CAMTP, University of Maribor, Slovenia (30 min)
3. David Benjamin Villasenor Perez, CAMTP, University of Maribor and UNAM, Mexico (30 min)
4. Matic Orel, CAMTP, University of Maribor (20 min)

MS4 Session Title: Neural Dynamics Across Spatiotemporal Scales: Models, Learning Processes, Computational Tools, and Clinical Applications

Session Organisers:
Manos, Thanos, ETIS Lab, CY Cergy Paris Université, France
email: thanos.manos@cyu.fr 
Torcini, Alessandro, LPTM, CY Cergy Paris Université, France
email: alessandro.torcini@cyu.fr 
Provata, Astero, Institute of Nanoscience and Nanotechnology, National Center for Scientific Research “Demokritos”, Greece
email: a.provata@inn.demokritos.gr

Neural networks and collective behavior represent leading research frontiers in complex systems theory. The human brain exemplifies a complex neural network, self-organizing into emergent states essential for its functions. Neural activity within individual nodes is often modeled using systems of ordinary differential equations. Such dynamical models offer a theoretical framework to explore the interplay between localized population dynamics and the intricate topology of brain networks, shedding light on overall brain activity. They also account for various forms of plasticity, such as synaptic and structural plasticity, and their influence on the system’s dynamics. This dynamical-modeling approach bridges concepts from mathematics, physics, and dynamical systems theory with empirically observed phenomena. For instance, it establishes connections between attractors, bifurcations, synchronization, and empirical neuroimaging data, including blood-oxygen-level-dependent (BOLD) signals from functional magnetic resonance imaging (fMRI). This mini symposium will feature expert-led discussions on the latest advancements in complex networks, collective behavior, synchronization phenomena, self-organization, neurocomputational platforms, and their diverse clinical applications.

  1. Thanos Manos, ETIS Lab, CY Cergy Paris Université, France
    Email: thanos.manos@cyu.fr
  2. Alessandro Torcini, LPTM, CY Cergy Paris Université, France
    Email: alessandro.torcini@cyu.fr
  3. Astero Provata, Institute of Nanoscience and Nanotechnology, National Center for Scientific Research “Demokritos”, Athens, Greece
    Email: a.provata@inn.demokritos.gr
  4. Viktor Jirsa, Institut de Neurosciences des Systèmes, Aix-Marseille University, France
    Email: viktor.jirsa@univ-amu.fr
  5. Alex Leow, University of Illinois Chicago, USA
    Email: weihliao@uic.edu
  6. Diego Pazo, Instituto de Física de Cantabria, University of Cantabria, Spain
    Email: pazo@ifca.unican.es
  7. Andrey Shilnikov, Neuroscience Institute, Georgia State University
    Email: ashilnikov@gsu.edu
  8. Roberto Barrio, Computational Dynamics Group (CoDy), Departamento de Matemática Aplicada, University of Zaragoza, Spain
    Email: rbarrio@unizar.es
  9. Simona Olmi, Institute for Complex Systems, Florence, Italy
    Email: simona.olmi@gmail.com
  10. Johanne Hizanidis, Institute of Electronic Structure & Laser (IESL/FORTH)
    Email: hizanidis@physics.uoc.gr
  11. Jeroen van Schependom, AIMS lab, Center for Neurosciences, Vrije Universiteit Brussel, Belgium.
    Email: jeroen.van.schependom@vub.be
  12. Oleh Omel’chenko, Department of Physics and Astronomy, University of Potsdam, Germany
    Email: oleh.omelchenko@uni-potsdam.de
  13. Sandra Diaz-Pier, Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany
    Email: s.diaz@fz-juelich.de
  14. Klaus Lehnertz, Neurophysics Group, Department of Epileptology, Medical Center, University of Bonn, Germany
    Email: Klaus.Lehnertz@ukbonn.de
  15. Raphael Bergoin, University Medical Center Hamburg-Eppendorf, Germany
    Email: raphael.bergoin@gmail.com
  16. Pau Clusella Coberó, Department of Mathematics, Universitat Politècnica de Catalunya
    Email: pau.clusella@upc.edu

MS5 Session Title: Applications of ordinal patterns-based complexity quantifiers for experimental time-series

Session Organisers:
Pattanayak, Arjendu K., Department of Physics and Astronomy, Carleton College, Northfield, USA
email: arjendu@carleton.edu 
Aragoneses, Andrés, Physics Faculty, Whitman College, Walla Walla, USA
email: aragonea@whitman.edu

Recent advances in time-series analysis have highlighted the value of ordinal patterns-based complexity quantifiers as robust tools for understanding dynamical systems. These measures, based on permutation entropy and other metrics applied to symbolic analysis of the time-series, provide a computationally efficient means of uncovering hidden patterns, underlying temporal symmetries, and regime changes in experimental data. Applications span diverse fields such as neuroscience, climatology, finance, quantum chaos, and photonics, including the characterization of time-series regularity, detection of transitions in complex systems, and classification of families of chaos.

In this mini-symposium we want to bring together researchers to explore the breadth of applications for these methods from across the fields listed above. Topics include, but are not limited to, the characterization of time-series regularity, detection of transitions in complex systems, and quantification of complex dynamics.

By focusing on both theoretical advancements and real-world implementations, this symposium aims to foster interdisciplinary dialogue and stimulate innovation in the analysis of experimental time-series data.

Juan Gancio (juan.gancio@upc.edu)
Jordi Tiana-Alsina (jordi.tiana@ub.edu)
Cristina Masoller (cristina.masoller@upc.edu)
Giulio Tirabassi (giulio.tirabassi@udg.edu)
María Duque (maria.duque@upc.edu)
Miguel C. Soriano (miguel@ifisc.uib-csic.es)
Xavier Porte (javier.porte-parera@strath.ac.uk)
Inmaculada Leyva (inmaculada.leyva@urjc.es)
Juan A. Almendral (juan.almendral@urjc.es)

MS6 Session Title: Patterns of synchrony and asynchrony in oscillatory media and networks

Session Organisers:
Pikovsky, Arkady, Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany,
email: arkady.pikovsky@gmail.com

Nepomnyashchy, Alexander, Department of Mathematics, Technion – Israel Institute of Technology, Haifa, Israel,
email: nepom@technion.ac.il

It is known that the same nonlinear system can perform synchronous oscillations or display asynchronous or disordered behavior, depending on some delicate differences in parameters and conditions. The synchronization can happen despite a disorder within the system, while synchronous oscillations can be spontaneously destroyed by internal instabilities. The synchronous and disordered dynamics can coexist. The goal of the minisymposium is to present the panorama of phenomena related to synchronization and desynchronization in networks of oscillators and oscillatory media, and elucidate their common features and dynamic patterns.

Part I

Arkady Pikovsky, Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany.
arkady.pikovsky@gmail.com

Simona Olmi, Institute of Complex Systems, CNR in Sesto Fiorentino, Florence, Italy,
simona.olmi@fi,isc.cnr.it

Rok Cestnik, Center for Mathematical Sciences, Lund University, Lund, Sweden.
rok.cestnik@math.lth.se

Oleh Omelchenko, Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany,
oleh.omelchenko@uni-potsdam.de

Part 2

Dmitry Turaev, Dynam IC, Imperial College London, London, UK, d.turaev@imperial.ac.il

Idan Sorin, Department of Mathematics, Technion – Israel Institute of Technology, Haifa, Israel,
idansorin@campus.technion.ac.il

Astero Provata, Institute of Nanoscience and Nanotechnology, National Center for Scientific
Research “Demokritos”, Athens, Greece, a.provata@inn.demokritos.gr

Seungjae Lee, Chair for Network Dynamics, Technical University of Dresden, Dresden, Germany,
seungjae.lee@tu-dresden.de

MS7 Session Title: Recent trends in dynamical chaos: from theory to applications

Session Organisers:
Kazakov, Alexey, University Higher School of Economics, Nizhny Novgorod, Russia
email: kazakovdz@yandex.ru

Turaev, DmitryImperial College London, London, UK
email: dturaev@imperial.ac.uk

Sinelshchikov, Dmitry
Biofisika Institute (CSIC-UPV/EHU), Leioa, Spain
email: disine@gmail.com

The minisymposium is devoted to modern aspects and trends in the theory of dynamical chaos, with focus on higher-dimensional problems. It will cover various topics: dissipative chaos, reversible systems, and Hamiltonian dynamics, theory of local and global bifurcations, theory of pseudohyperbolic, hyperchaotic, and spiral attractors, as well as applications.

1. Dongchen Li
Shanghai University, China
d.li@imperial.ac.uk
2. Arturo Vieiro
Barcelona University, Spain
vieiro@maia.ub.es
3. Ainoa Murillo
Barcelona University, Spain
amurillo@ub.edu
4. Alexey Kazakov
HSE University, Nizhny Novgorod, Russia
kazakovdz@yandex.ru
5. Dmitry Sinelshchikov
Biofisika Institute (CSIC-UPV/EHU), Leioa, Spain
disine@gmail.com
6. Efrosiniia Karatetskaia
HSE University, Nizhny Novgorod, Russia
eyukaratetskaya@gmail.com
7. Klim Safonov
HSE University, Nizhny Novgorod, Russia
safonov.klim@yandex.ru
8. Vyacheslav Kruglov
Carl von Ossietzky University of Oldenburg, Germany
kruglovyacheslav@gmail.com

MS8 Session Title: Dynamics of physiological networks: from function to malfunction

Session Organisers:
Schöll, Eckehard, Institute for Theoretical Physics, Technische Universität Berlin, Berlin, Germany 
email: schoell@physik.tu-berlin.de

Andrzejak, Ralph Gregor, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
email: ralph.andrzejak@upf.edu

In the human organism, multi-component physiological systems, each with its own regulatory mechanism, continuously interact to coordinate their functions in an integrated network. The dynamics resulting from these interactions exhibits emergent patterns of synchronization or desynchronization which are associated with healthy or pathological states. Examples include tumor dynamics in physiological multilayer networks of organs and the immune system; brain network dynamics; psychiatric interactions of consciousness and decision making, among others. Often phase transitions and critical phenomena occur in such dynamical networks, leading to a plethora of partial synchronization patterns and complex collective behavior.

1. Frederico Costa
Oncology Department, Hospital Sírio Libanês, São Paulo, Brazil
2. Deniz Eroglu
Faculty of Engineering and Natural Sciences, Kadir Has University, 34083 Istanbul, Turkey
Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
3. Simona Olmi
CNR-Consiglio Nazionale delle Ricerche—Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy
4. Günter Schiepek
Institute of Synergetics and Psychotherapy Research, Paracelsus Medical University, Salzburg, Austria
University Hospital of Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical University, Salzburg, Austria
Faculty of Psychology and Educational Sciences, Ludwig Maximilians University, Munich, Germany

Reservoir computing leverages the response of driven dynamic systems for data-driven modeling, time-series prediction, classification, and control. Historically, recurrent neural networks have primarily served as dynamic reservoirs, but recent developments have extended this general approach to a variety of substrates, including passive and active optical systems, electro-optical systems, magnetic systems, memristors, spin wave systems, swarms, skyrmion textures, biological tissue, and quantum systems. A common goal of these investigations is to improve our theoretical understanding of various reservoir computing approaches and to achieve fast and energy-efficient hardware implementations. This minisymposium brings together experts across these areas to foster new ideas and cooperations which is vital towards advancing reservoir computing.

1. Lina Jaurigue (Technische Universität Ilmenau, Germany), lina.jaurigue@tu-ilmenau.de
2. Ulrich Parlitz (Max Planck Institut for Dynamics and Self-Organization, Göttingen, Germany), ulrich.parlitz@ds.mpg.de
3. Hiromichi Suetani (Oita University, Japan), hsue@icloud.com
4. Christoph Räth (DLR München, Germany),5. christoph.raeth@dlr.de
5. Martin Trefzer (University of York, UK), martin.trefzer@york.ac.uk
6. Miquel C. Soriano (Campus Universitat de les Illes Balears, IFISC, Mallorca, Spain) , miguel@ifisc.uibcsic.es
7. Andrew Flynn (University College Cork, Ireland), andrewflynn@ucc.ie
8. Kathy Lüdge (Technische Universität Ilmenau, Germany), kathy.luedge@tu-ilmenau.de
9. Guy van der Sande (VUB Brussels, Belgium), guy.van.der.sande@vub.be

Machine learning tools have rapidly become integral to the analysis of data from dynamical systems, offering new perspectives on data-driven model development. While classical, theory-driven approaches may rely on simplifying assumptions, purely data-driven methods risk overfitting and can fail to illuminate understanding of the underlying dynamical relationships. Hybrid methods that integrate theoretical knowledge with data-driven components promise a new class of models and open opportunities for automation in the process of scientific discovery. Yet questions persist about how to interpret these models: Do they reveal or obscure key interactions and dynamics compared to more traditional approaches? And what is the scientist’s role in shaping and understanding such frameworks? This minisymposium invites contributions from across the sciences that employ machine learning and AI to identify relationships among dynamic variables and forecast future behavior. We welcome both theoretical and applied work, with the goal of fostering an interdisciplinary dialogue on the benefits, limitations, and emerging opportunities of machine learning for dynamical systems and real-world modeling applications.

1. Pascal Nieters, Institute of Cognitive Science, University of Osnabrück/ Rahel Vortmeyer-Kley, Institute for Chemistry and Biology of Marine Environment, Carl von Ossietzky Universität Oldenburg
(Navigating to the right solution when learning bifurcating dynamical systems)

2. Gianmarco Ducci, Fritz-Haber-Institut der Max-Planck-Gesellschaft Berlin
(A robust approach for sparse parametric data driven modeling)

3. Lukas Stelz, FIAS, University Frankfurt
(Inferring Local Transmission Networks from Epidemiological Incidence Data)

4. Sedighe Raeisi, Institute of Cognitive Science, University of Osnabrück
(Computational discovery of individual differences in cognitive mechanisms)

This mini-symposium will focus on the development and application of concepts from dynamical systems theory and data analysis to develop new approaches for the diagnosis and treatment of heart diseases. Speakers come from different fields, ranging from basic research to clinical applications, will present their work on the following topics: simulation and control of (chaotic) excitation dynamics in the myocardium, methods to terminate life-threatening cardiac fibrillation, time series analysis to diagnose pathological changes in physiological signals and to quantify the interaction of the heart with other organs, new machine learning methods for analysis and data-driven modeling of complex spatio-temporal data.

1. Omer Berenfeld (University of Michigan, Ann Arbor, USA)

2. Tim de Coster (Leiden University Medical Center, Leiden, Netherlands)

3. Daniel Frühwald (Technische Hochschule Nürnberg, Nürnberg, Germany)

4. Beata Graff (Medical University of Gdańsk, Gdańsk, Poland)

5. Inga Kottlarz (Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany)

6. Alessandro Loppini (Campus Bio-Medico University of Rome, Rome, Italy)

7. Marta Varela (Imperial College London, London, UK)

8. Justine Wolter (Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany)

9. Sandra Zarychta (Jagiellonian University Medical College, Kraków, Poland)

MS12 Session Title: Nonlinear Waves meets Stochastic Dynamics

Session Organisers:
Logioti, Anna, Institute of Analysis, Dynamics and Modeling, University of Stuttgart, Stuttgart, Germany
email: anna.logioti@mathematik.uni-stuttgart.de

Hilder, Bastian, Department of Mathematics, Technical University of Munich, Munich, Germany
email: bastian.hilder@tum.de

Nonlinear waves are ubiquitous in natural systems ranging from fluid convection and vegetation systems to cardiac dynamics. Since these systems also naturally exhibit noise, the mathematical analysis of patterns and nonlinear waves in stochastic systems has received much attention over the last years. This minisympoium aims to bring together researchers from both stochastic analysis, dynamical systems, pattern formation, and nonlinear waves to present an overview of the field, give insight into recent results and identify open questions which can only be answered by combining the expertise of both communities.

1. Christian Kuehn (TU Munich, Germany) (confirmed)
2. Mark van den Bosch (Leiden University, The Netherlands)
3. Alexandra Blessing (University of Konstanz, Germany)
4. Barbara Gentz (University of Bielefeld, Germany)
5. Christian Hamster (University of Amsterdam, The Netherlands)
6. Nils Berglund (Universit´e d’Orl´eans, France)
7. Tobias Hurth (Universit´e de Neuchˆatel, Switzerland)
8. Xue-Mei Li (Imperial College London, UK)

The extraordinary complexity of biological systems and the understanding of such systems requires the integration of concepts and methods from physics, mathematics and computer science. This minisymposium will explore recent advances in the study of complex biophysical systems, focusing on developments in modelling methods, theoretical frameworks and computational techniques.

The minisymposium topics include theoretical frameworks, mechanistic insights, and applications of complexity science in biology, statistical physics methods, and data-driven and machine-learning modelling approaches. Special attention is given to the application of these methods to neuroscience, biophysics, biological rhythms, and ecosystem population dynamics.

Through talks by experts from multiple disciplines, the aim is to promote insightful discussions on theoretical and applied aspects of the cutting-edge challenges arising from the complexity of biological systems, as well as innovative related technologically approaches, and to facilitate interdisciplinary collaboration to contribute to the further development of the field.

1. Ying-Cheng Lai, Arizona State University, USA
2. Junjie Jiang, Xi’an Jiaotong University, China
3. Daniel Koch, Max Planck Institute for Neurobiology of Behavior, Germany
4. Pierre Haas, Max Planck Institute for the Physics of Complex Systems, Max Planck Institute of Molecular Cell Biology and Genetics, and Center for Systems Biology Dresden, Germany
5. Bernd Blasius, Carl von Ossietzky University Oldenburg
6. Bo-Wei Qin, Fudan University, China
7. Yu Meng, University of Oldenburg

MS14 Session Title: Current Trends in Network Dynamics

Session Organisers:
Martens, Erik Andreas, Centre for Mathematical Science, Lund University, Lund, Sweden
email: erik.martens@math.lth.se

Omelchenko, Oleh, Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany,
email: oleh.omelchenko@uni-potsdam.de

Wolfrum, Matthias, Weierstrass Insitute, Berlin, Germany,
email: wolfrum@wias-berlin.de

Bick, Christian, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands 
email: cbick@vu.nl

Complex networks are pivotal in understanding a wide range of systems in physics, chemistry, biology, neuroscience, and socio-economic domains. Dynamical networks consist of interacting units that can exhibit complex collective behaviors and patterns. This minisymposium covers the following topics:
Adaptation / Co-evolution networks where the dynamics of the network and the dynamics on the network are interdependent are crucial for understanding synaptic plasticity, learning, neurodegenerative diseases, with applications in chemical, epidemic, biological, transport, social systems, power grids, and climate networks.
Higher order networks allow for direct interactions of more than two units. They provide a comprehensive framework for modeling complex systems, induce dynamical phenomena, and pose new challenges for their mathematical understanding.
Symmetries, bifurcations, phase reduction, and normal forms are important mathematical tools to investigate dynamical systems. Recently, there have been new developments to adapt them to specific types of network systems.
Synchronization control in neural networks: Many concepts of network dynamics find their application in neuroscience. Various methods for detecting and controlling synchrony are crucial in neural networks and neuroscience, with ongoing research offering potential for new discoveries.

1. Jürgen Kurths, PIK, DE Juergen.Kurths@pik-potsdam.de
2. Felix Augustsson, Centre for Mathematical Sciences, Lund University, SE, felix.augustsson@math.lth.se
3, Jaeyoung Yoon, TU Munich, DE, jaeyoung.yoon@tum.de
4. Babette de Wolff, University Hamburg, DE, babette.de.wolff@uni-hamburg.de
5. Eddie Nijholt, University of São Paulo, Brazil, eddie.nijholt@gmail.com
6. Jeff Moehlis, Department of Mechanical Engineering, University of California, Santa Barbara, USA, moehlis@engineering.ucsb.edu
7. Michael Rosenblum, Inst. of Physics and Astronomy, University of Potsdam, Germany, mros@uni-potsdam.de
8. Matthias Wolfrum, Weierstrass Institute, Berlin, DE, wolfrum@wias-berlin.de
9. Erik A. Martens, Centre for Mathematical Sciences, Lund University, SE, erik.martens@math.lth.se

MS15 Session Title: Data-driven dynamics: learning, reduction, and analysis

Session Organisers:
Kalia, Manu, Department of Mathematics and Computer Science, Freie Universitaet Berlin, Berlin, Germany
email: m.kalia@fu-berlin.de

Froyland, Gary, School of Mathematics and Statistics, University of New South Wales, Sydney, Australia
email: g.froyland@unsw.edu.au

The broad goal of modelling with dynamical systems is to construct differential equation-based formalisms to replicate observed data. Typically this is done using first principles methods by identifying underlying physical laws. Off late, data-driven techniques inspired by reduced order modelling and machine learning have been successful in learning dynamical systems directly from observed data, even when the state is not directly observed. Machine learning can also be leveraged to learn solution operators for PDEs with the physics-informed formalism. In this minisymposium we explore recent advances in domains such as kernel methods, forecasting, parameter identification and reduced order modelling.

1. Kathrin Padberg-Gehle (Leuphana University Lueneberg, Germany, kathrin.padberg-gehle@leuphana.de)
2. Boumedine Hamzi (Caltech/Imperial College London/Alan Turing Institute, UK, boumedine.hamzi@gmail.com)
3. Zheng Bian (Clarkson Center for Complex Systems Science, USA, zheng@bian-zheng.cn)
4. Gary Froyland (University of New South Wales, Australia, g.froyland@unsw.edu.au)
5. Manu Kalia (Freie Universitaet Berlin, Germany, m.kalia@fu-berlin.de)

MS16 Session Title: Data-driven methods in complex dynamical systems

Session Organisers:
Vlachas, Pantelis, ETH Zurich, Zurich, Switzerland;  Ai2C Technologies AG; Zurich, Switzerland
email: pvlachas@ethz.ch

Kavallaris, Nikos, Karlstad University, Karlstad, Sweden
email
: Nikos.Kavallaris@kau.se

Spiliotis, Konstantinos, School of Civil Engineering, Democritus University, Xanthi, Greece
email
: kspiliot@civil.duth.gr

The mini-symposium highlights key topics at the forefront of recent discussions within the Dynamics Days community. It explores novel data-driven approaches in dynamical systems, including manifold learning, diffusion maps, machine learning for scientific computing, such as physics-informed neural networks (PINNs), topological data analysis, and system identification concerning both experimental and theoretical works.
Bringing together interdisciplinary research, the symposium spans a wide range of complex systems, including computational biology, computational neuroscience, mathematical epidemiology, climate dynamics, ecological modeling, mobility and crowd dynamics, as well as complex fluids and materials science.
By bringing together researchers from these diverse fields, the symposium fosters cross-disciplinary insights, enabling a deeper and more nuanced extraction of information from experiments than conventional approaches allow.

1. Dr. Nikos Kavallaris, Karlstad University, Sweden
2. Dr. Haralampos Hatzikirou, Khalifa University, UAE
3. Dr. Ioannis Tsoukalas, Democritus University of Thrace, Xanthi, Greece.
4. Dr. Avrillia Konguetsof, Democritus University of Thrace, Xanthi, Greece.
5. Dr. Constantinos Siettos, University of Naples Federico II, Italy.
6. Giannis Papadimitriou, Democritus University of Thrace, Xanthi, Greece.
7. Dr. Jens Starke, University of Rostock, Germany.
8. Niklas Kruse, University of Rostock, Germany.

MS17 Session Title: Nonlinear and chaotic dynamics in photonics and phononics

Session Organisers:
Kuwashima, Fumiyoshi, Otemon Gakuin University, Japan
email: f12_kuwashima@outlook.jp

Isoshima, Takashi, Center for Advanced Photonics, RIKEN, Japan
email
: isoshima@riken.jp

The proposed mini-symposium aims at presenting some recent theoretical and experimental progresses on nonlinear, complex and chaotic dynamics in the field of photonics and phononics.
Chaos, instability, and nonlinear dynamics have attracted a lot of attention in recent years in photonics. Laser chaos induced by delayed optical feedback is one of them, and applied to, for example, generate Terahertz (THz) waves efficiently, This is an emergence of Chaotic Supremacy, the new concept to express superior functionality realized only by chaos. Optical bistable device with spatial expanse can also generate chaos using an external refractory feedback, that may achieve pulse network with chaotic time series. Also, in phononics, nonlinear dynamics provides interesting anomalous heat transport.
In view of such situation, we intend in this minisymposium to bridge various photonics and phononics researches including THz technology, nonlinear optics, and nonlinear phononics, to stimulate further studies in these research fields.

1. Fumiyoshi Kuwashima (Otemon Gakuin University)
2. Takashi Isoshima (RIKEN)
3. Kazuyuki Yoshimura (Faculty of Engineering, Tottori University)
4. Tomoki Yamagami (Tokyo University), yamagami-tomoki-qwb@g.ecc.u-tokyo.ac.jp
5. Shoma Ohara (Tokyo University of Technology), oharasm@stf.teu.ac.jp
6. Alexandre Locquet (GeorgiaTech-CNRS), alocquet@georgiatech-metz.fr
7. Miguel C. Soriano or I. Fischer (IFISC), miguel@ifisc.uib-csic.es

While a large body of work in nonlinear dynamics is devoted to the long-term behavior of complex systems in form of attractors, recent research emphasizes more and more that transient dynamics is of equal importance when we consider a large variety of phenomena like metastability, chimera states, rate-induced tipping or, more general, non-autonomous dynamical systems. Metastability refers here to a switching process between different dynamical regimes in state space with certain properties (e.g. periodic or chaotic), where each of these regimes lasts for a rather long but transient time before switching/transiting to another such regime. These transient phenomena as well as transitions between metastable states are often mediated by dynamical states of saddle character, such as saddle fixed points, saddle periodic orbits or chaotic saddles, which are the unstable counter parts of chaotic attractors. This minisymposium will discuss the emergence of long transients as well as metastability either related to the existence of chaotic saddles or to steady-states of saddle character, their statistical properties and their importance for various applications in complex systems. The minisymposium will also discuss concepts and methods to identify metastable regimes. The applications will address e.g. climate phenomena, collective motion of agents, neuronal systems, the dynamics of the heart, and ecosystem dynamics. Finally, we will also address the role of noise leading sometimes to extremely long transients.

1. Ulrike Feudel, University Oldenburg, Germany
2. Manuel Adams, University Bonn, Germany
3. Chittaranjan Hens, Hyderabad, India
4. Valerio Lucarini, University Leicester, UK
5. Reyk Börner, University Utrecht, The Netherlands
6. Aneta Koseska, Max Planck Institute of Neurobiology of Behavior Bonn, Germany
7. Ehud Meron, University Ber Sheva, Israel
8. Peter Koltai, University Bayreuth, Germany
9. Kathrin Padberg-Gehle, University Lüneburg, Germany
10. Ayanava Basak, Jadavpur University Kolkata, India
11. Tomasz Kapitaniak, Polytechnical University Lodz, Poland

MS18 Session Title: Higher-order network dynamics

Session Organiser:
Mölter, Jan, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
email: jan.moelter@tum.de

Complex systems are characterised by the pathways of interactions between their constituents. However, traditionally, these have been reduced to only pairwise interactions, when considering polyadic interactions might actually be more true to the system. In fact, as has been established in the last several years, this crucially affects the system’s phenomenology and, particularly, its emergent dynamics. In this minisymposium, we will discuss recent work concerning and exploring the effects of polyadic or higher-order interactions.

1. Péter Simon (Eötvös Loránd University)
2. Ana P. Millan (Universidad de Granada)
3. Jan Mölter (Technical University of Munich)
4. Christian Bick (Vrije Universiteit Amsterdam)