Search Results for author: Roland Schwan

Found 3 papers, 2 papers with code

On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach

1 code implementation15 May 2024 Roland Schwan, Nicolaj Schmid, Etienne Chassaing, Karim Samaha, Colin N. Jones

We present the identification of the non-linear dynamics of a novel hovercraft design, employing end-to-end deep learning techniques.

Position

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

no code implementations24 Jun 2023 Truong X. Nghiem, Ján Drgoňa, Colin Jones, Zoltan Nagy, Roland Schwan, Biswadip Dey, Ankush Chakrabarty, Stefano Di Cairano, Joel A. Paulson, Andrea Carron, Melanie N. Zeilinger, Wenceslao Shaw Cortez, Draguna L. Vrabie

Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of PIML models; and 4) physics-informed digital twins.

Physics-informed machine learning

Stability Verification of Neural Network Controllers using Mixed-Integer Programming

1 code implementation27 Jun 2022 Roland Schwan, Colin N. Jones, Daniel Kuhn

We provide sufficient conditions for the closed-loop stability of the candidate policy in terms of the worst-case approximation error with respect to the baseline policy, and we show that these conditions can be checked by solving a Mixed-Integer Quadratic Program (MIQP).

Model Predictive Control

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