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GalacticDynamics
unxt

Unitful Quantities in JAX

8K 60 4
oberbichler
hyperjet

Automatic differentiation with dual numbers

5K 17 4
abess-team
skscope

skscope: Sparse-Constrained OPtimization via itErative-solvers

3K 326 16
GalacticDynamics
unxt-api

Unitful Quantities in JAX

2K 60 4
NVIDIA
minkowskiengine

Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors

2K 3K 472
GalacticDynamics
unxt-hypothesis

Unitful Quantities in JAX

2K 60 4
oberbichler
hypergraph

Reversed mode second order automatic differentiation for python (WIP)

1K 4 0
adxorg
astrodynx

A modern astrodynamics library powered by JAX: differentiate, vectorize, JIT to GPU/TPU, and more

1K 10 4
GalacticDynamics
jax-quantity

Quantities in JAX

342 58 4
GalacticDynamics
galax

Galactic Dynamics in Jax.

266 44 8
WeiXuanChan
autod

Forward automatic differentiation

242 0 0
mntsx
thoad

**thoad** (Torch High Order Automatic Differentiation) is a lightweight reverse-mode autodifferentiation engine written entirely in Python that works over PyTorch’s computational graph to compute **high order partial derivatives**. Unlike PyTorch’s native autograd - which is limited to first-order native partial derivatives - **thoad** is able to performantly propagate arbitray-order derivatives throughout the graph, enabling more advanced gradient-based computations.

54 6 1