Approximation error of operator learning for parabolic PDE | BIMSA Thursday Machine Learning Applications Seminar-Qiuzhen College,Tsinghua University

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[BIMSA-Tsinghua Seminar on Machine Learning and Differential Equations] From Neural PDEs to Neural Operators: Blending data and physics for fast predictions
Abstract:We will review physics-informed neural network and summarize available extensions for applications in computational mechanics and beyond. We will also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. The universal approximation theorem of operators is suggestive of the potential of NNs in learning fro...