Autoregressive long-horizon prediction of plasma edge dynamics

TL;DR

This paper introduces transformer-based autoregressive surrogates trained on high-fidelity plasma edge simulation data, enabling fast, stable, and accurate long-horizon predictions of plasma dynamics for fusion device modeling.

Contribution

The work develops and evaluates long-horizon autoregressive transformer surrogates trained on SOLPS-ITER data, significantly improving prediction stability and speed for plasma edge dynamics.

Findings

Longer autoregressive horizons improve prediction stability.

Surrogates are orders of magnitude faster than traditional simulations.

Prediction accuracy decreases in untrained physical regimes.

Abstract

Accurate modeling of scrape-off layer (SOL) and divertor-edge dynamics is vital for designing plasma-facing components in fusion devices. High-fidelity edge fluid/neutral codes such as SOLPS-ITER capture SOL physics with high accuracy, but their computational cost limits broad parameter scans and long transient studies. We present transformer-based, autoregressive surrogates for efficient prediction of 2D, time-dependent plasma edge state fields. Trained on SOLPS-ITER spatiotemporal data, the surrogates forecast electron temperature, electron density, and radiated power over extended horizons. We evaluate model variants trained with increasing autoregressive horizons (1-100 steps) on short- and long-horizon prediction tasks. Longer-horizon training systematically improves rollout stability and mitigates error accumulation, enabling stable predictions over hundreds to thousands of steps and reproducing key dynamical features such as the motion of high-radiation regions. Measured end-to-end wall-clock times show the surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration. Prediction accuracy degrades when the surrogate enters physical regimes not represented in the training dataset, motivating future work on data enrichment and physics-informed constraints. Overall, this approach provides a fast, accurate surrogate for computationally intensive plasma edge simulations, supporting rapid scenario exploration, control-oriented studies, and progress toward real-time applications in fusion devices.