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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.
Real-time Nonlinear MPC Strategy with Full Vehicle Validation for Autonomous Driving
In this paper, we present the development and deployment of an embedded optimal control strategy for autonomous driving applications on a Ford Focus road vehicle.
A Generalized Representer Theorem for Hilbert Space - Valued Functions
The necessary and sufficient conditions for existence of a generalized representer theorem are presented for learning Hilbert space-valued functions.
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