Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
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Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Why do we need physics-informed machine learning (PIML)?, by Shuai Zhao
Operator Learning via Physics-Informed DeepONet: Let's Implement It From Scratch, by Shuai Guo
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators - University of Wales Trinity Saint David
SVD perspectives for augmenting DeepONet flexibility and interpretability - ScienceDirect
Operator Learning via Physics-Informed DeepONet: Let's Implement It From Scratch, by Shuai Guo
Algorithms, Free Full-Text
arxiv-sanity
Deep transfer operator learning for partial differential equations under conditional shift
Operator Learning via Physics-Informed DeepONet: Let's Implement It From Scratch, by Shuai Guo
Introduction to Hybrid Modelling for Digital Twins
The DeepONets for Finance: An Approach to Calibrate the Heston Model
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