Reinforcement learning-based estimation for partial differential equations

arXiv:2302.01189v2 Announce Type: replace
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 …

abstract arxiv cs.lg cs.sy design differential dynamics eess.sy errors filters however low projects reinforcement reinforcement learning space state systems tractable type