Rheology-informed Machine Learning Projects
-
Updated
Apr 8, 2024 - Jupyter Notebook
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.
Learning function operators with neural networks.
Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."
This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs
Physics-Informed Neural networks for Advanced modeling
PDEBench: An Extensive Benchmark for Scientific Machine Learning
Learning in infinite dimension with neural operators.
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."