Applied Math Seminar

Teaching Machines Fusion Physics: Interpretable ML for Safer, Smarter Plasmas

Speaker: Cristina Rea, MIT

Location: Warren Weaver Hall 1302

Date: Friday, November 7, 2025, 2:30 p.m.

Synopsis:

Machine Learning (ML) and Artificial Intelligence (AI) are increasingly transforming fusion research, complementing and, in many cases, surpassing traditional statistical tools. These methods are accelerating progress by enabling more accurate modeling, optimized strategies, and enhanced experimental realization [1].

In this seminar, we will focus on the development of interpretable ML-driven metrics for two critical challenges in magnetic confinement fusion: (1) real-time monitoring of proximity to plasma stability boundaries [2,3,4] and (2) the optimization of plasma trajectories [5,6]. By emphasizing interpretability, these approaches not only deliver predictive power but also provide insights that are actionable for control and disruption prevention.

A key element of this work is the use and development of JAX frameworks [7,8], which enable seamless integration of physics equations with neural networks. This hybrid modeling approach accelerates system identification for plasma dynamics, advancing the reliability, efficiency, and scalability of ML-enabled solutions for both existing and next-generation fusion devices – a key focus of the MIT PSFC Disruptions Team (https://disruptions.mit.edu/)

References:

  1. Rea J.l of Fusion Energy 44, 39 (2025) https://doi.org/10.1007/s10894-025-00509-z
  2. Rea, IAEA Fusion Energy Conference Proceedings EX/P1–25 (2021)
  3. Barr et al., Nucl. Fusion 61, 126019 (2021)
  4. Maris, Rea et al., Nucl. Fusion 65, 016051 (2025)
  5. Wang, Rea et al., Comm. Physics (2025) https://www.nature.com/articles/s42005-025-02146-6
  6. Wang, Pau, Rea et al., Nature Communications (accepted, 2025) https://arxiv.org/pdf/2502.12327v2
  7. Bradbury et al., (2018) http://github.com/jax-ml/jax
  8. Wang et al., IEEE TPS (submitted, 2025)