separable-operator-networks

A library for scientific machine learning and physics-informed learning

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

Final projects for 401-4656-21L AI in Sciences and Engineering @ ETHz. Includes implementation of Fourier Neural Operator (FNO) with time dependency, data-driven symbolic regression with PDE-Find and foundation model based on FNO for phase-field dynamics

Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."

NodeLab is a simple MATLAB-repository for node-generation and adaptive refinement for testing, and implementing various meshfree methods (including physics-informed neural networks, PINNs and DeepOnet) for solving PDEs in arbitrary domains.

PyTorch implemention of the Position-induced Transformer for operator learning in partial differential equations