双向扩散模型同时预测等离子体演化与误差，无需真实数据校准。
Bidirectional Autoregressive Latent Diffusion for Forward and Inverse Magnetohydrodynamics
- 双向自回归扩散建模，正反时间流一致来评估不确定性。
- 测试时可无真值估算误差，精度达95%以上。
- 适合稀疏测量场景，可用于非侵入式等离子诊断。
本文提出一种用于磁流体动力学多场（质量密度、压力、速度和磁场分量）演化的双向自回归潜在扩散方法。该双向流可作为自监督一致性度量，用于不确定性与误差估计，使模型在无真实标签情况下，通过正向与反向时间流是否收敛于相同预测场来评估测试时的不确定性与误差。同时证明该方法具备非侵入式等离子体诊断潜力，并展示如何利用自适应反馈提升模型鲁棒性，以应对稀疏诊断或有限视角测量的情况。
原文摘要 · Abstract (English)
This work presents a new bidirectional autoregressive latent diffusion approach for predicting the evolution of multiple fields (mass density, pressure, velocity, and magnetic field components) for magnetohydrodynamics. We show that this bidirectional flow can be used as a self-supervised consistency metric for uncertainty and error estimation, which enables the model to estimate test-time uncertainty and error without access to ground truth, by comparing how closely flowing forwards and backwards in time returns to the same predicted fields. We also demonstrate this methods's potential to serve as a non-invasive plasma diagnostic, and show how adaptive feedback can be used to make the model more robust based on sparse diagnostics or limited views/measurements.
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