Oryx is a library for probabilistic programming and deep learning built on top of Jax.

Simulation-based inference in JAX

sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has …

Dingo: Deep inference for gravitational-wave observations

Gravitational-wave data analysis tools in Jax

Cryo electron microscopy image simulation and analysis built on JAX.

Gym of Examples for Profiling Heterogeneity Methods in Cryo-EM

Normalizing-flow enhanced sampling package for probabilistic inference in Jax

Code for Gaussian Score Matching Variational Inference

BridgeStan provides efficient in-memory access through Python, Julia, and R to the methods of a Stan model.

Deep learning quantum Monte Carlo for electrons in real space

Mathematical Introduction to Electronic Structure Theory

A package for binary and continuous, single and multi-material, truss and continuum, 2D and 3D topology optimization on unstructured meshes using automatic differentiation in Julia.

Data-driven dynamical systems toolbox.

Classical and quantum Monte Carlo simulations in Julia

Fermi quantum chemistry program

Algebraic Multigrid in Julia

Various implementations of the classical SIR model in Julia

Fast samplers for Neyman-Scott processes.

A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language

Active multi-fidelity Bayesian online changepoint detection.

Bayesian inference with probabilistic programming.

Implementation of variational Bayes inference algorithms