Physics-informed neural networks via stochastic Hamiltonian dynamics learning

In this paper, we propose novel learning frameworks to tackle optimal control problems by applying the Pontryagin maximum principle and then solving for a Hamiltonian dynamical system. Applying the Pontryagin maximum principle to the original optimal control problem shifts the learning focus to redu...

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Vydané v:	arXiv.org
Hlavní autori:	Bajaj, Chandrajit, Nguyen, Minh
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Ithaca Cornell University Library, arXiv.org 26.04.2024
Predmet:	Learning Maximum principle Optimal control Robustness Stochastic models
ISSN:	2331-8422
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