{"id":47771154,"url":"https://github.com/ergodicio/ml-for-lpi","last_synced_at":"2026-04-03T09:40:38.349Z","repository":{"id":264413167,"uuid":"892904485","full_name":"ergodicio/ml-for-lpi","owner":"ergodicio","description":null,"archived":false,"fork":false,"pushed_at":"2025-12-05T17:26:31.000Z","size":1878,"stargazers_count":0,"open_issues_count":0,"forks_count":2,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-12-09T04:59:09.009Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ergodicio.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2024-11-23T02:41:55.000Z","updated_at":"2025-12-05T17:26:36.000Z","dependencies_parsed_at":"2025-09-15T21:02:52.294Z","dependency_job_id":"e7f95556-4b3a-4aa8-89d1-37b4009461c0","html_url":"https://github.com/ergodicio/ml-for-lpi","commit_stats":null,"previous_names":["ergodicio/ml-for-lpi"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ergodicio/ml-for-lpi","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ergodicio%2Fml-for-lpi","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ergodicio%2Fml-for-lpi/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ergodicio%2Fml-for-lpi/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ergodicio%2Fml-for-lpi/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ergodicio","download_url":"https://codeload.github.com/ergodicio/ml-for-lpi/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ergodicio%2Fml-for-lpi/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31344873,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-03T08:03:20.796Z","status":"ssl_error","status_checked_at":"2026-04-03T08:00:37.834Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-04-03T09:40:35.921Z","updated_at":"2026-04-03T09:40:38.343Z","avatar_url":"https://github.com/ergodicio.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Laser-Plasma Instability Minimization using Differentiable Simulators\nThis repo contains code used for gradient-based minimization of laser plasma instabilities (LPI) using ADEPT-LPSE\n\n### The repo\nThis repository shows how to extend `ADEPT` by using one of its existing solvers to perform gradient-based optimization.\n\nThe code is of 3 different categories\n1. Python scripts that run `ADEPT` in an optimization loop or parameter scan\n2. Configuration `yaml` files for `ADEPT`\n3. Module files that extend the `ADEPT` functionality by providing parameterized inputs, loss functions, and postprocessing functions\n\n### The physics\nWe solve the slowly-varying envelope approximation for modeling electron plasma waves driven at a quarter critical surface by a laser beam.\n\n### The optimization problem\nWe 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. \n\n### Generative Neural Reparameterization\nRather 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.\n\n### ADEPT\n`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. ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fergodicio%2Fml-for-lpi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fergodicio%2Fml-for-lpi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fergodicio%2Fml-for-lpi/lists"}