91 citations · 107 across the 7 of their papers we have counts for
3 papers · 1 filter
Reinforcement learning-based estimation for partial differential equations
Saviz Mowlavi, Mouhacine Benosman
In systems governed by nonlinear partial differential equations such as fluid flows, the design of state estimators such as Kalman filters relies on a reduced-order model (ROM) tha…
Physics-Informed Koopman Network
Yuying Liu, Aleksei Sholokhov, Hassan Mansour +1
Koopman operator theory is receiving increased attention due to its promise to linearize nonlinear dynamics. Neural networks that are developed to represent Koopman operators have…
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
Yangchen Pan, Amir-massoud Farahmand, Martha White +3
Recent work has shown that reinforcement learning (RL) is a promising approach to control dynamical systems described by partial differential equations (PDE). This paper shows how…