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Author:
Leon Armbruster
Summary:
Differentiable numerical methods in JAX (FEM, FDM, spectral) with scientific machine learning
Latest version:
0.4.0
Required dependencies:
blackjax
|
cloudpickle
|
einops
|
equinox
|
fenics-basix
|
foundax
|
jax
|
matfree
|
meshio
|
optax
|
orbax-checkpoint
|
paramax
|
pygmsh
|
scipy
|
shapely
Optional dependencies:
black
|
cupy-cuda12x
|
flake8
|
flax
|
fmmax
|
iree-base-compiler
|
iree-base-runtime
|
jax
|
jax-numerical-operators
|
jaxamg
|
mmgpy
|
mypy
|
nevergrad
|
nvidia-cudss-cu12
|
nvmath-python
|
paramax
|
pyfiglet
|
pymetis
|
pypardiso
|
pytest
|
xprof
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