Feb 23, 2024 · The aim of this work is to develop deep learning-based algorithms for high-dimensional stochastic control problems based on physics-informed learning and ...

For solving the optimal control of stochastic systems, there are two powerful tools: Pontryagin's maximum principle Bismut1978 ; Bensoussan1983 ; Pontrygin1987 ...

Feb 27, 2024 · The aim of this work is to develop deep learning-based algorithms for high-dimensional stochastic control problems based on physics-informed ...

A pathwise operator associated with the HJB equation is introduced so that a problem of physics-informed learning is defined and two numerical methods are ...

Feb 27, 2024 · Neural optimal controller for stochastic systems via pathwise HJB operator ⋆. February 2024. Authors: Zhe Jiao at Northwestern Polytechnical ...

Neural optimal controller for stochastic systems via pathwise HJB operator · Emergence of heavy tails in homogenized stochastic gradient descent.

Neural optimal controller for stochastic systems via pathwise HJB operator ... The aim of this work is to develop deep learning-based algorithms for high- ...

CrossRef. [1], Neural optimal controller for stochastic systems via pathwise HJB operator. arXiv preprint arXiv:2402.15592, 2024. No relevant information. JMF ...

This work presents a continuous-time approach based on statistical linearization techniques for the efficient computation of optimal open-loop controls.

Fusion-Mamba for Cross-modality Object Detection · Neural optimal controller for stochastic systems via pathwise HJB operator · GMTalker: Gaussian Mixture based ...