91 citations · 213 across the 33 of their papers we have counts for
5 papers · 1 filter
Dual parametric and state estimation for partial differential equations
Saviz Mowlavi, Mouhacine Benosman
Designing estimation algorithms for systems governed by partial differential equations (PDEs) such as fluid flows is challenging due to the high-dimensional and oftentimes nonlinea…
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
Xiangyuan Zhang, Weichao Mao, Saviz Mowlavi +2
We introduce controlgym, a library of thirty-six industrial control settings, and ten infinite-dimensional partial differential equation (PDE)-based control problems. Integrated wi…
Global Convergence of Receding-Horizon Policy Search in Learning Estimator Designs
Xiangyuan Zhang, Saviz Mowlavi, Mouhacine Benosman +1
We introduce the receding-horizon policy gradient (RHPG) algorithm, the first PG algorithm with provable global convergence in learning the optimal linear estimator designs, i.e.,…
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…
Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning
James Queeney, Mouhacine Benosman
Many real-world domains require safe decision making in uncertain environments. In this work, we introduce a deep reinforcement learning framework for approaching this important pr…