Learning in infinite dimension with neural operators.
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Updated
Jun 11, 2024 - Python
Learning in infinite dimension with neural operators.
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
Code for Characterizing Scaling and Transfer Learning Behavior of FNO in SciML
An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.
Solving multiphysics-based inverse problems with learned surrogates and constraints
Neural Operator-Assisted Computational Fluid Dynamics in PyTorch
Code to reproduce the results in "Conditional score-based diffusion models for Bayesian inference in infinite dimensions", NeurIPS 2023
The first GAN-based tabular data synthesizer integrating the Fourier Neural Operator for global dependency imitation
Code for the paper "The Random Feature Model for Input-Output Maps between Banach Spaces"
Code for the paper ``Error Bounds for Learning with Vector-Valued Random Features''
Implementation of Fourier Neural Operator from scratch
Fokker Planck based Data Assimilation method using Fourier Neural Operators as integrator
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