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Training neural networks to approximate operators that map one function to another.
Distinct from Operator Mappings: Distinct from ML framework operator mappings; this refers to learning the mathematical operator itself.
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DeepXDE is a scientific machine learning library and deep learning PDE solver used to compute solutions for forward and inverse ordinary, partial, and integro-differential equations. It functions as a physics-informed neural network library that embeds physical laws and boundary conditions directly into the neural network loss function. The project provides a deep operator network framework for learning operator mappings that approximate relationships between functions in multiphysics problems. It is implemented as a multi-backend tensor library, allowing the system to switch between differen
Trains neural networks to approximate mathematical operators that map functions to other functions using aligned datasets.