Theory Guided and Interpretable Neural Operator Design for Partial Differential Equation Learning
cs.LG, cs.CE
Submitted: 2026-09-27
Updated: 2026-09-27
Code: https://github.com/lavenderses/NSsimulation
Terminology
Sources
- Data-Driven Finite Elements Methods: Machine Learning Acceleration of Goal-Oriented Computations
- Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
- Fourier Neural Operator for Parametric Partial Differential Equations
- DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
- Single-shot prediction of parametric partial differential equations
- Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
- Variational Autoencoding of PDE Inverse Problems
- Factorized Fourier Neural Operators
- Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
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