Disentanglement for improved data-driven modeling of dynamical systems (2025)
Abstract / truncated to 115 words
Modeling dynamical systems is a fundamental task in various scientific and engineering domains, requiring accurate predictions, robustness to varying conditions, and interpretability of the underlying mechanisms. Traditional data-driven approaches often struggle with long-term prediction accuracy, generalization to out-of-distribution (OOD) scenarios, and providing insights into the system's behavior. This thesis explores the integration of supervised disentanglement into deep learning models as a means to address these challenges. We begin by advancing the state-of-the-art in modeling wave propagation governed by the Saint-Venant equations. Utilizing U-Net architectures and purposefully designed training strategies, we develop deep learning models that significantly improve prediction accuracy. Through OOD analysis, we highlight the limitations of standard deep learning models in capturing complex spatiotemporal ...
dynamical systems – machine learning – physics-based
Information
- Author
- Stathi Fotiadis
- Institution
- Imperial College London
- Supervisor
- Publication Year
- 2025
- Upload Date
- March 27, 2025
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