mlsanityData to 14 Oct 2026

SafeDivertor: Faithful Divertor Heat Flux Reconstruction from Macroscopic Plasma State Signals via Time-Frequency Prior Exploitation

arXiv 2608.05669 ↗First posted 6 Aug 2026physics.plasm-ph cs.AI cs.CVcited by 0

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Abstract (arXiv)

Divertor heat-flux analysis is essential for understanding plasma-wall interactions and protecting plasma-facing components in magnetic-confinement fusion devices, while conventional infrared-based inversion is usually performed after discharge and requires heat-conduction modeling with device-specific material properties, divertor geometry, and boundary conditions. Rather than accelerating this conventional infrared-based inversion paradigm, we introduce a new online-oriented signal-based reconstruction paradigm that directly reconstructs time-resolved radial heat-flux profiles from multi-source macroscopic plasma-state signals available during discharge. To enable systematic study of this task, we construct DivMPS2HF, a multi-source discharge dataset that provides the data foundation and benchmark for signal-based divertor heat-flux reconstruction. We further propose SafeDivertor, a task-driven framework designed to address the key challenges of signal-based heat-flux reconstruction. It employs physical prior-aware initialization to provide radial-distribution guidance for target channels, input perturbation to reduce over-reliance on specific heterogeneous signals, spectral-aware reconstruction optimization to exploit time-frequency priors and preserve transient dynamics, and progressive training to stabilize the optimization of these complementary objectives. Experiments on DivMPS2HF demonstrate that SafeDivertor achieves the best overall performance among the evaluated time-series baselines across all five metrics, establishing a new performance benchmark for signal-based divertor heat-flux reconstruction. The source code will be released on https://github.com/Event-AHU/OpenFusion

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 33 references that are in the map. First posted on arXiv 6 Aug 2026.

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References · 17

  1. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  2. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 290
  3. 2024TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variablescited by 248
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  9. 2024A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challengescited by 47
  10. 2025CrossLinear: Plug-and-Play Cross-Correlation Embedding for Time Series Forecasting with Exogenous Variablescited by 22
  11. 2025Audio Super-Resolution with Latent Bridge Modelscited by 14
  12. 2026GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variablescited by 13
5 more
  1. 2025Revisiting Heat Flux Analysis of Tungsten Monoblock Divertor on EAST using Physics-Informed Neural Networkcited by 2
  2. 2026T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputationcited by 2
  3. 2025Sonnet: Spectral Operator Neural Network for Multivariable Time Series Forecastingcited by 1
  4. 2025HGTS-Former: Hierarchical HyperGraph Transformer for Multivariate Time Series Analysiscited by 1
  5. 2025TimePerceiver: An Encoder-Decoder Framework for Generalized Time-Series Forecastingcited by 1

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