Mini-Workshop: Data-driven Modeling, Analysis, and Control of Dynamical Systems

  • Clarence W. Rowley

    Princeton University, Princeton, USA
  • Claudia Schillings

    Freie Universität Berlin, Berlin, Germany
  • Karl Worthmann

    Technische Universität Ilmenau, Ilmenau, Germany
Mini-Workshop: Data-driven Modeling, Analysis, and Control of Dynamical Systems cover
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Abstract

With the rapid increase in data resources and computational power as well as the accompanying current trend to incorporate machine learning into existing methods, data-driven approaches for modelling, analysis, and control of dynamical systems have attracted new interest and opened doors to novel applications. However, there is always a discrepancy between mathematical models and reality such that rigorously-shown error bounds and uncertainty quantification are indispensable for a reliable use of data-driven techniques, e.g., using surrogate models in optimisation-based control. Similar comments apply to data-enhanced models. Consequently, uncertainty about parameters, the model itself and numerous other aspects need to be taken into account, e.g., in data-driven control of (stochastic) dynamical systems. Hence, the respective paradigm changes have led to a variety of novel concepts which, however, still suffer from limitations: many concentrate only on a single aspect, are only applicable to systems of limited complexity, or lack a sound mathematical foundation including guarantees on feasibility, robustness, or the overall performance. Pushing these limits, we face a wide spectrum of theoretic and algorithmic challenges in modeling, analysis, and control under uncertainty using data-driven methods.

Cite this article

Clarence W. Rowley, Claudia Schillings, Karl Worthmann, Mini-Workshop: Data-driven Modeling, Analysis, and Control of Dynamical Systems. Oberwolfach Rep. 21 (2024), no. 4, pp. 3255–3300

DOI 10.4171/OWR/2024/57