Scientific Machine Learning Python Packages

deepxde

A library for scientific machine learning and physics-informed learning

jaxns

Probabilistic Programming and Nested sampling in JAX

tesseract-core

Universal components for differentiable scientific computing. :package:

diffeqpy

Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

verifiable-labs

RL environments for scientific AI agents with conformal-calibrated rewards

tesseract-jax

Execute + differentiate Tesseracts as part of JAX programs, with full support for function transformations like JIT, grad, and more. ⚡

opinf

Operator Inference for data-driven model reduction of dynamical systems.

jwave

A JAX-based research framework for differentiable and parallelizable acoustic simulations, on CPU, GPUs and TPUs

pyplaid

A package that implements a data model tailored for AI and ML in the context of physics problems

jax-neural-operators

JAX library for neural operator and PDE foundation model training.

vlabs-calibrate

RL environments for scientific AI agents with conformal-calibrated rewards

verifiable-labs-envs

RL environments for scientific AI agents with conformal-calibrated rewards

lirisimpact

Repository contaning the original code of IMPACT algorithm, an interpretable model for ordinal predictions with multi-class outputs"

phaethon

End-to-End Physics-Constrained Scientific Computing & Sci-ML Framework

tensordiffeq

Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

verifiable-labs-code-humaneval-multiturn

RL environments for scientific AI agents with conformal-calibrated rewards

rom-operator-inference

Operator inference for data-driven, non-intrusive model reduction of dynamical systems.

reactorch

ReacTorch: A Differentiable Reacting Flow Simulation Package in PyTorch

modularml

Modular, fast, and reproducible ML experimentation built for R&D.

unitflex

End-to-End Physics-Constrained Scientific Computing & Sci-ML Framework

idrlnet

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

koopmanlab

A library for Koopman Neural Operator with Pytorch.

separable-operator-networks

Official repo for separable operator networks -- extreme-scale operator learning for parametric PDEs.

fastvpinns

FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries