3 papers
physics.plasm-ph2025
Data-driven multi-species heat flux closures for two-stream-unstable plasmas with nonlinear sparse regression
Emil R. Ingelsten, Madox C. McGrae-Menge, E. Paulo Alves +1
The dual aims of accuracy and computational efficiency in computational plasma physics lend themselves well to the use of fluid models. The first of these goals, however, is only s…
physics.plasm-ph2025
Embedding physical symmetries into machine-learned reduced plasma physics models via data augmentation
Madox C. McGrae-Menge, Jacob R. Pierce, Frederico Fiuza +1
Machine learning is offering powerful new tools for the development and discovery of reduced models of nonlinear, multiscale plasma dynamics from the data of first-principles kinet…
physics.plasm-ph2024
Data-driven discovery of a heat flux closure for electrostatic plasma phenomena
Emil R. Ingelsten, Madox C. McGrae-Menge, E. Paulo Alves +1
Progress in understanding multi-scale collisionless plasma phenomena requires employing tools which balance computational efficiency and physics fidelity. Collisionless fluid model…