A generic interface for linear algebra backends

A Large-Scale Climate Model Dataset for Machine Learning

Toolbox for Differential Geometry on Triangle and Tetrahedra Meshes (FEM, Laplace, Poisson, Heat Flow ...)

Dynamic inference from single-cell snapshots by optimal transport

Artificial Intelligence Research for Science (AIRS)

A Python implementation of Chebfun

🤖 Machine Learning Summer School deadlines

This repository contains code released by DiffEqML Research

Awesome resources on normalizing flows.

Official code for the paper "Physics-Informed Implicit Representations of Equilibrium Network Flows"

A generalised Gaussian process method for learning vector fields over non-Euclidean domains. Particularly useful for EEG data analysis and to regularise vector fields using global structures.

[ICLR 2023] Symmetries, flat minima, and the conserved quantities of gradient flow

A package for computing data-driven approximations to the Koopman operator.

Algorithms for computations on random manifolds made easier

A curated collection of resources and research related to the geometry of representations in the brain, deep networks, and beyond

About A collection of AWESOME things about information geometry Topics

Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs

Python package for solving partial differential equations using finite differences.

megaman: Manifold Learning for Millions of Points

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

CompleX Group Interactions (XGI) is a Python package for higher-order networks.

Computations and statistics on manifolds with geometric structures.

GREAD: Graph Neural Reaction-Diffusion Networks, ICML 2023

ICML 2022, Finding Global Homophily in Graph Neural Networks When Meeting Heterophily

GraphCON (ICML 2022)

List of papers studying machine learning through the lens of category theory

Using graph network to solve PDEs