Collaborative Research: Enabling multi-scale studies of magnetic reconnection with interpretable data-driven models
Loureiro, Nuno, Fiuza, Frederico, Alves, Eduardo Paolo et al. · 2026
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Description
A four-university team closes award DE-SC0022012 with a final technical report on enabling multi-scale studies of magnetic reconnection with interpretable data-driven models. The period ran from September 1, 2021 to August 31, 2025. Frederico Fiuza of Stanford was lead principal investigator. Nuno Loureiro at MIT, E. Paulo Alves at UCLA, and William Dorland at the University of Maryland joined him. Reconnection is the sudden rearrangement of magnetic fields behind solar flares and tokamak disruptions.
The project goal was better reduced models: fluid descriptions that keep kinetic accuracy. The team built machine learning tools on sparse and symbolic regression. The tools pull readable equations, partial differential equations a physicist can inspect, out of first-principles particle simulation data. Earlier proof-of-principle work had recovered the known hierarchy of plasma equations from such data. Here the target was kinetic closures for reconnection, plus models of how reconnection injects and accelerates particles.
Catalog record
| CREATOR | Loureiro, Nuno, Fiuza, Frederico, Alves, Eduardo Paolo et al. |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 15 KB |
| LICENSE | Public Domain |
| RIGHTS | PUBLIC DOMAIN |
| SOURCE | DOE OSTI |