Reinforcement learning-based estimation for partial differential equations

• URL: http://arxiv.org/abs/2302.01189v2
• Date: Thu, 4 Apr 2024 14:35:35 GMT
• Title: Reinforcement learning-based estimation for partial differential equations
• Authors: Saviz Mowlavi, Mouhacine Benosman,
• Abstract summary: In systems governed by nonlinear partial differential equations such as fluid flows, the design of state estimators relies on a reduced-order model (ROM) We introduce the reinforcement learning reduced-order estimator (RL-ROE) in which the correction term that takes in the measurements is given by a nonlinear policy trained through reinforcement learning.
• Score: 1.621267003497711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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) that projects the original high-dimensional dynamics onto a computationally tractable low-dimensional space. However, ROMs are prone to large errors, which negatively affects the performance of the estimator. Here, we introduce the reinforcement learning reduced-order estimator (RL-ROE), a ROM-based estimator in which the correction term that takes in the measurements is given by a nonlinear policy trained through reinforcement learning. The nonlinearity of the policy enables the RL-ROE to compensate efficiently for errors of the ROM, while still taking advantage of the imperfect knowledge of the dynamics. Using examples involving the Burgers and Navier-Stokes equations, we show that in the limit of very few sensors, the trained RL-ROE outperforms a Kalman filter designed using the same ROM. Moreover, it yields accurate high-dimensional state estimates for trajectories corresponding to various physical parameter values, without direct knowledge of the latter.

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