A Physics-Informed Deep Operator Learning Framework Based on Separable Nonlinear Least 
Square Formulation for Uncertainty Quantification

Author:

Chang Cheng,Zeng Tieyong

Publisher

Elsevier BV

Reference25 articles.

1. DGM: A deep learning algorithm for solving partial differential equations;Justin Sirignano;Journal of Computational Physics,2018

2. The deep Ritz method: A deep learning-based numerical algorithm for solving variational problems;E Weinan;Communications in Mathematics and Statistics,2017

3. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations;M Raissi;Journal of Computational Physics,2019

4. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators;Lu Lu;Nature Machine Intelligence,2021

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