jcallaham Goto Github PK

data-driven-discretization-1d

Code for "Learning data-driven discretizations for partial differential equations"

diff-zoo

Differentiation for Hackers

diffrax

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

e3nn

A modular framework for neural networks with Euclidean symmetry

firedrake

Firedrake is an automated system for the portable solution of partial differential equations using the finite element method (FEM)

jcallaham.github.io

micrograd

A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

nekflows

Python utilities and standard flow configurations for Nek5000 CFD simulation

population-annealing

Data and code accompanying 2017 PRE paper with Jon Machta

robust-flow-reconstruction

Code for "Robust flow field reconstruction from limited measurements vis sparse representation" (J. Callaham, K. Maeda, and S. Brunton 2018)

siamcse21

Resources for the SIAMCSE21 minitutorial "Automatic Differentiation as a Tool for Computational Science"

torchcontrol

A PyTorch library for all things nonlinear control and reinforcement learning.

torchdyn

A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods