Laser-Plasma Instability Minimization using Differentiable Simulators

This repo contains code used for gradient-based minimization of laser plasma instabilities (LPI) using ADEPT-LPSE

The repo

This repository shows how to extend ADEPT by using one of its existing solvers to perform gradient-based optimization.

The code is of 3 different categories

1. Python scripts that run ADEPT in an optimization loop or parameter scan
2. Configuration yaml files for ADEPT
3. Module files that extend the ADEPT functionality by providing parameterized inputs, loss functions, and postprocessing functions

The physics

We solve the slowly-varying envelope approximation for modeling electron plasma waves driven at a quarter critical surface by a laser beam.

The optimization problem

We want to minimize the LPI that occurs in a simulation. The free parameters are those that parameterize the bandwidth of the driving laser. Because our simulation is differentiable, we can take a gradient of the simulation with respect to the free parameters.

Generative Neural Reparameterization

Rather than find just one set of optimal bandwidth parameters, we can choose to learn a generative function that learns the distribution of optimal parameters. This method is described in Joglekar, A. S. Generative Neural Reparameterization for Differentiable PDE-constrained Optimization. Preprint at http://arxiv.org/abs/2410.12683 (2024). This repo provides the code for this method.

ADEPT

ADEPT is a differentiable plasma physics simulation tool. It can be found at https://github.com/ergodicio/adept. This particular set of solvers uses a JAX adaptation of the Laser-Plasma Simulation Environment developed at UR-LLE.