7 citations · 9 across the 7 of their papers we have counts for
13 papers
A Deep Learning Approximation of Non-Stationary Solutions to Wave Kinetic Equations
Steven Walton, Minh-Binh Tran, Alain Bensoussan
We present a deep learning approximation, stochastic optimization based, method for wave kinetic equations. To build confidence in our approach, we apply the method to a Smoluchows…
Control in Hilbert Space and First Order Mean Field Type Problem
Alain Bensoussan, Henry Hang Cheung, Sheung Chi Phillip Yam
We extend the work \cite{bensoussan2019control} by two of the coauthors, which dealt with a deterministic control problem for which the Hilbert space could be generic and investiga…
Value-Gradient based Formulation of Optimal Control Problem and Machine Learning Algorithm
Alain Bensoussan, Jiayue Han, Sheung Chi Phillip Yam +1
Optimal control problem is typically solved by first finding the value function through Hamilton-Jacobi equation (HJE) and then taking the minimizer of the Hamiltonian to obtain th…
Machine Learning and Control Theory
Alain Bensoussan, Yiqun Li, Dinh Phan Cao Nguyen +3
We survey in this article the connections between Machine Learning and Control Theory. Control Theory provide useful concepts and tools for Machine Learning. Conversely Machine Lea…
Identification of linear dynamical systems and machine learning
Alain Bensoussan, Fatih Gelir, Viswanath Ramakrishna +1
The topic of identification of dynamic systems, has been at the core of modern control , following the fundamental works of Kalman. Realization Theory has been one of the major out…
Mean Field approach to stochastic control with partial information
Alain Bensoussan, Sheung Chi Phillip Yam
The classical stochastic control problem under partial information can be formulated as a control problem for Zakai equation, whose solution is the unnormalized conditional probabi…