Skip to content
/ jax-pf Public

Differentiable pattern forming simulations with finite difference and pseudospectral methods implemented in Jax.

Notifications You must be signed in to change notification settings

acoh64/jax-pf

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

32 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

JAX-PF

Differentiable pattern forming simulations with finite difference and pseudospectral methods implemented in jax.

ch_test.mp4

Inspiration from:

In particular, much of the code was modeled after jax-cfd, including Domain class, the equation classes, and the timestepping and anti-aliasing utilities.

Many of these models are commonly used in materials science. For example, the Gross-Pitaevskii equation is used to model Bose-Einstein condensates and general ultracold quantum gases. In addition, the Cahn-Hilliard equation occurs in phase separation in liquids and solids, including biomolecular condensates in cells and lithium ion battery electrode materials.

To create a conda environment, use conda env create -f environment.yml

Depending on your CUDA drivers, you may need to install a different version of Jax.

Check out this interactive Google Colab demo to get started.

About

Differentiable pattern forming simulations with finite difference and pseudospectral methods implemented in Jax.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published