Online identification and control of PDEs via reinforcement learning methods
type: Item
instance of: scholarly article
title: Online identification and control of PDEs via reinforcement learning methods
zbMATH Open document ID: 1547.65165
author: Alessandro Alla
author: Agnese Pacifico
author: Michele Palladino
author: Andrea Pesare
published in: Advances in Computational Mathematics
publication date: 2024-09-17T00:00:00Z
Mathematics Subject Classification ID: 65M99
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68Q32
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 49N35
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 35B35
Mathematics Subject Classification ID: 34A34
zbMATH DE Number: 7914906
zbMATH Keywords: reinforcement learning
zbMATH Keywords: system identification
zbMATH Keywords: stabilization of PDEs
zbMATH Keywords: state-dependent Riccati equations
zbMATH Keywords: Bayesian linear regression
zbMATH Keywords: numerical approximation
MaRDI profile type: Publication
cites work: State-dependent Riccati equation feedback stabilization for nonlinear PDEs
cites work: Nonlinear feedback controllers and compensators: a state-dependent Riccati equation approach
cites work: Optimal control and viscosity solutions of Hamilton-Jacobi-Bellman equations
cites work: The theory of dynamic programming
cites work: Q3286740
cites work: Exponential stability and stabilization of extended linearizations via continuous updates of Riccati‐based feedback
cites work: Discovering governing equations from data by sparse identification of nonlinear dynamical systems
cites work: Data-Driven Modeling and Control of Complex Dynamical Systems Arising in Renal Anemia Therapy
cites work: Statistical Models
cites work: Nonlinear model predictive control. Theory and algorithms.
cites work: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
cites work: Jacobian-free Newton-Krylov methods: a survey of approaches and applications.
cites work: Q5755052
cites work: A new algorithm for the LQR problem with partially unknown dynamics
cites work: Approximate Dynamic Programming
cites work: From reinforcement learning to optimal control: a unified framework for sequential decisions
cites work: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
cites work: Q3394879
cites work: Data-Driven Identification of Parametric Partial Differential Equations
cites work: Q4626283
DOI: 10.1007/S10444-024-10167-Y
description: scientific article; zbMATH DE number 7914906