adept is a suite of differentiable numerical solvers for plasma physics, built in JAX. It covers kinetic physics (Vlasov-Poisson/Maxwell, Vlasov-Fokker-Planck), laser-plasma envelope equations, and fluid models — with all solvers end-to-end differentiable via automatic differentiation.
This differentiability is the key feature: it allows simulations to be embedded in gradient-based optimization and machine learning workflows. We use ADEPT to solve inverse problems, discover physics, and train neural network closures — applications that would be intractable with conventional simulation tools.
Unsupervised discovery of nonlinear plasma physics using differentiable kinetic simulations — [Journal of Plasma Physics]
Deep learned closure model for nonlinear Landau damping — [IOP Machine Learning: Science and Technology]
Two additional papers currently in review.
The code is [here]