Bayesian inference with probabilistic programming.

Gaussian processes in JAX.

Geometric kernels on manifolds, meshes and graphs

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

Implementation of DeepMind's Deep Generative Model of Radar (DGMR) https://arxiv.org/abs/2104.00954

Surrogate modeling and optimization for scientific machine learning (SciML)

A Python package for interactive geospaital analysis and visualization with Google Earth Engine.

TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data

Probabilistic time series modeling in Python

MLX: An array framework for Apple silicon

Bayesian Modeling and Probabilistic Programming in Python

The official SuiteSparse library: a suite of sparse matrix algorithms authored or co-authored by Tim Davis, Texas A&M University.

Adaptive Experimentation Platform

Bayesian optimization in PyTorch

Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

A highly efficient and modular implementation of Gaussian Processes in PyTorch

⚡ Dynamically generated stats for your github readmes

Testing out Diffusion-based models for weather and PV forecasting

This repository contains implementations and illustrative code to accompany DeepMind publications

Examples of how to create colorful, annotated equations in Latex using Tikz.