Search Results for author:
Found 2 papers, 1 papers with code
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
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.
Stability Verification of Neural Network Controllers using Mixed-Integer Programming
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).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
