用神经算子替代黑洞吸积模拟，加速研究且能捕捉等离子体破裂细节。
Learning Neural Operator Surrogates for the Black Hole Accretion Code
- 引入物理信息损失项，让模型在无数据时段仍能推演动态变化。
- 在电阻率跨越甜-帕克与快速重联区时，成功复现了等离子体碎裂过程。
- 首次将神经算子直接应用于自适应网格的高分辨率磁流体模拟，适合天体物理计算加速研究者。
广义相对论磁流体（GR-MHD）模拟对研究黑洞吸积、相对论喷流和磁重联至关重要，但计算成本极高，限制了参数系统的探索。本文针对黑洞吸积代码（BHAC）生成的两个天体物理场景，研究神经算子代理模型。首先，在特殊相对论电阻磁流体（SRRMHD）的Orszag-Tang涡旋演化中，训练物理信息傅里叶神经算子（PINO），覆盖从甜-帕克到快速重联的电阻率范围。通过在更细时间粒度上嵌入控制方程作为额外损失项，模型在无模拟数据的时间点仍能学习动力学行为，成功恢复了数据仅监督模型无法再现的等离子体碎裂现象。据我们所知，这是首个将物理信息神经算子应用于特殊相对论电阻磁流体的研究，也是首个探究此类模型解析等离子体碎裂能力的工作。其次，采用OFormer风格的Transformer神经算子，在特殊相对论磁流体（SRMHD）的脊-鞘喷流演化上进行训练。模型直接作用于自适应网格，凸显长序列下线性注意力的必要性。该代理模型能捕捉大部分主要特征，尤其在早期预测中表现优异。据我们所知，这是首次将神经算子直接应用于高分辨率自适应网格精细磁流体模拟。
原文摘要 · Abstract (English)
General-relativistic magnetohydrodynamic (GR-MHD) simulations are essential for studying black hole accretion, relativistic jets, and magnetic reconnection, yet their computational cost severely limits systematic parameter exploration. We investigate neural operator surrogates for two astrophysically relevant simulation scenarios produced by the Black Hole Accretion Code (\texttt{BHAC}). First, a Physics Informed Fourier Neural Operator (PINO) is trained on the special-relativistic resistive MHD (SRRMHD) evolution of the Orszag-Tang vortex over a range of resistivities spanning the Sweet-Parker and fast reconnection regimes. By embedding the governing equations as an additional loss term evaluated at finer temporal resolution than the available data supervision, the model learns dynamics at time steps where no simulation data is provided, enabling recovery of plasmoid formation that a data-only baseline trained on the same sparse snapshots fails to reproduce. To our knowledge, the present work is the first application of a physics informed neural operator to special relativistic resistive MHD, and the first to investigate the capability of such models to resolve plasmoid formation in SRRMHD. In a second line of investigation, an OFormer-style Transformer Neural Operator is trained on the evolution of spine-sheath relativistic jets created with \texttt{BHAC}, in special-relativistic MHD (SRMHD). The model is directly applied on the adaptive mesh, highlighting the need for linear attention due to long sequences. The neural surrogate model is capable of capturing most of the major details, especially in early predictions. To our knowledge, this constitutes the first application of a neural operator directly on a high resolution adaptive mesh refinement grid in the context of MHD simulations.
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