Original paper
Optimal Control of Pdes Using Physics-Informed Neural Networks
Abstract
Physics-informed neural networks (PINNs) have recently become a popular method for solving forward and inverse problems governed by partial differential equations (PDEs). By incorporating the residual of the PDE into the loss function of a neural network-based surrogate model for the unknown state, PINNs can seamlessly blend measurement data with physical constraints. Here, we extend this framework to PDE-constrained optimal control problems,...
Paper Details
Title
Optimal Control of Pdes Using Physics-Informed Neural Networks
Published Date
Jan 1, 2022
Journal
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