Autoregressive long-horizon prediction of plasma edge dynamics

• URL: http://arxiv.org/abs/2512.23884v1
• Date: Mon, 29 Dec 2025 22:19:27 GMT
• Title: Autoregressive long-horizon prediction of plasma edge dynamics
• Authors: Hunor Csala, Sebastian De Pascuale, Paul Laiu, Jeremy Lore, Jae-Sun Park, Pei Zhang,
• Abstract summary: We present transformer-based, autoregressive surrogates for efficient prediction of plasma edge state fields.<n>Trained on SOLPS-ITER data, the surrogates forecast electron temperature, electron density, and radiated power over extended horizons.
• Score: 4.152161649392119
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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