We would like to draw your attention to the Minisymposium on “Data-Driven Uncertainty and Sensitivity Analysis for Dynamical Systems" as part of the coming WCCM-ECCOMAS 2026 congress (https://wccm-eccomas2026.org/). The description of the MS is available here: https://wccm-eccomas2026.org/event/area/817b2370-ab83-11f0-bce5-000c29ddfc0c. The abstract can also be found at the end of this email.
When and where? July, 19 – 24, Munich, Germany.
Abstract submission deadline: January 12, 2026
We welcome contributions to our MS, considering your aligned research interests. Please share this announcement with colleagues who may be interested.
We are looking forward to seeing you in Munich,
Dimitrios Loukrezis (CWI Amsterdam)
Kerstin Lux-Gottschalk (TU Eindhoven)
Ulrich Römer (TU Braunschweig)
MS148 - Data-Driven Uncertainty and Sensitivity Analysis for Dynamical Systems
The qualitative and quantitative analysis of dynamical systems is crucial for understanding numerous real-world systems and phenomena, from weather prognosis and biological networks, to medical and engineering applications. However, the ubiquitous presence of uncertainty often renders the analysis of dynamical systems a difficult task. Therefore, methods for uncertainty quantification (UQ) in dynamical systems are evolving rapidly, with a particular emphasis on data-driven and machine learning approaches in recent years. At the same time, numerous challenges remain, particularly in the nonlinear setting. Exemplary challenges include, quantifying uncertainties on bifurcations and limit cycles; computationally managing high-dimensional uncertain states and inputs; deriving suitable sensitivity analysis metrics. This minisymposium aims to cover methodological work in the vast field of UQ in dynamical system analysis with a focus on data driven methods, related (but not limited) to:
• UQ for nonlinear dynamics (e.g., bifurcations, limit cycles).
• Data-driven forward and inverse UQ for dynamical systems.
• Combinations of Fourier analysis with UQ methods.
• Surrogate and reduced order modeling (incl. machine learning) for dynamical systems.
• Sensitivity analysis for dynamical systems.
References
[1] Partovizadeh, A., Schöps, S., and Loukrezis, D. "Fourier-enhanced reduced-order surrogate modeling for uncertainty quantification in electric machine design." Engineering with Computers (2025): 1-21.
[2] de Jong, L., Clasen, P., Müller, M., Römer, U. "Uncertainty analysis of limit cycle oscillations in nonlinear dynamical systems with the Fourier generalized Polynomial Chaos expansion." Journal of Sound and Vibration 607 (2025): 119017.
[3] Lux, K., Ashwin, P., Wood, R., and Kuehn, C. "Assessing the impact of parametric uncertainty on tipping points of the Atlantic meridional overturning circulation", Environ. Res. Lett. (2022) 17 075002.
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Dr. Kerstin Lux-Gottschalk
Assistant Professor – Centre for Analysis, Scientific Computing and Applications (CASA)
Eindhoven University of Technology, Department of Mathematics and Computer Science, Groene Loper 5, 5612 AE Eindhoven, Office 5.100
