continuiti

An elegant PyTorch deep reinforcement learning library.

Learning in infinite dimension with neural operators.

Code accompanying my blog post: So, what is a physics-informed neural network?

This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs

A Physics-Informed Neural Network to solve 2D steady-state heat equations.

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

A Library for Advanced Neural PDE Solvers.

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

[ICLR24] A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.