ADEPT
ADEPT is a set of A utomatic D ifferentiation E nabled P lasma T ransport solvers.
Examples
Examples can be found in the tests folder or in the adept-notebooks repository - http://github.com/ergodicio/adept-notebooks. Example configuration files are also provided in configs/
Documentation
Getting Started
Solvers
- Available Solvers
- Vlasov 1D1V Solver
- Vlasov 1D2V Solver
- Vlasov-2D Overview
- Vlasov-Fokker-Planck 1D Solver
- Vlasov-Fokker-Planck 2D Solver
- Joglekar 2014 reconstruction and hydro coupling
- Periodic MAGPIE carbon-flow initialization
- Review trigger: this page defines the physical-unit geometry contract for MAGPIE inputs.
- Driven interaction-region reservoirs
- Review trigger: this page defines the externally driven periodic reservoir scope.
- MAGPIE reference scales and flow analysis
- Review trigger: this page defines the analysis contract for MAGPIE-scale comparisons.
- Discrete electric work
- Review trigger: this page records the discrete electric-work correction and its limits.
- Spectrax 1D Solver
- Mixed Hermite-Legendre 1D Solver
- PIC 1D Solver
- FARSIGHT-1D
- LPSE 2D (Envelope-2D) Solver
- Two-Fluid 1D Solver
- OSIRIS adept module — usage overview
Configuration Reference
- Vlasov-1D Configuration Reference
- Vlasov-1D2V Configuration Reference
- Vlasov-2D Configuration Reference
- VFP-1D Configuration Reference
- Spectrax-1D Configuration Reference
- Mixed Hermite-Legendre 1D Configuration Reference
- PIC-1D Configuration Reference
- FARSIGHT-1D configuration
- LPSE-2D (Envelope-2D) Configuration Reference
- VFP-2D Configuration
- Two-Fluid-1D Configuration Reference
- OSIRIS Configuration Reference
- WarpX Configuration Reference
Reference
Note
This project is under active development.
Contributing Guide
The contributing guide is in development but for now, just make an issue / pull request and we can go from there :)
Citation
If you are using this package for your research, please cite
A. Joglekar and A. Thomas, “ADEPT - automatic differentiation enabled plasma transport,” ICML - SynS & ML Workshop (https://syns-ml.github.io/2023/contributions/), 2023
References
[1] A. S. Joglekar & A. G. R. Thomas. “Unsupervised discovery of nonlinear plasma physics using differentiable kinetic simulations.” J. Plasma Phys. 88, 905880608 (2022).
[2] A. S. Joglekar and A. G. R. Thomas, “Machine learning of hidden variables in multiscale fluid simulation,” Mach. Learn.: Sci. Technol., vol. 4, no. 3, p. 035049, Sep. 2023, doi: 10.1088/2632-2153/acf81a.