Learning in infinite dimension with neural operators.
-
Updated
May 23, 2024 - Python
Learning in infinite dimension with neural operators.
PDEBench: An Extensive Benchmark for Scientific Machine Learning
Physics-Informed Neural networks for Advanced modeling
This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs
Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."
Learning function operators with neural networks.
Rheology-informed Machine Learning Projects
Implementation of Fourier Neural Operator from scratch
Official implementation of Operator-ProbConserv: OOD UQ for Neural Operators
[ICLR24] A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values
Manifold Learning for Scientific Applications with SciML Interface.
Add a description, image, and links to the neural-operators topic page so that developers can more easily learn about it.
To associate your repository with the neural-operators topic, visit your repo's landing page and select "manage topics."