用神经网络加速天体物理辐射传输计算，快100倍且误差低于3%。
Emulating Radiative Transfer in Astrophysical Environments
- 采用傅里叶神经算子结合U-Net构建代理模型，模拟时变三维单色辐射传输。
- 相比传统方法速度提升超100倍，平均相对误差低于3%。
- 适合需要实时辐射反馈的高精度流体模拟，如星系演化研究。
辐射传输是天体物理学中的基本过程，对解释观测结果和模拟电离辐射与光压引起的热力学及动力学反馈至关重要。然而，由于光与物质相互作用复杂，且光速远高于天体环境中气体速度，求解辐射传输方程计算成本极高，难以在流体动力学模拟中实时包含辐射效应。为此，我们提出一种基于傅里叶神经算子与U-Net结合的代理模型，可近似三维、单色、时变条件下的吸收-发射近似辐射传输，实现超过两个数量级的速度提升，平均相对误差保持在3%以下，展现了其在先进流体模拟中集成应用的巨大潜力。
原文摘要 · Abstract (English)
Radiative transfer is a fundamental process in astrophysics, essential for both interpreting observations and modeling thermal and dynamical feedback in simulations via ionizing radiation and photon pressure. However, numerically solving the underlying radiative transfer equation is computationally intensive due to the complex interaction of light with matter and the disparity between the speed of light and the typical gas velocities in astrophysical environments, making it particularly expensive to include the effects of on-the-fly radiation in hydrodynamic simulations. This motivates the development of surrogate models that can significantly accelerate radiative transfer calculations while preserving high accuracy. We present a surrogate model based on a Fourier Neural Operator architecture combined with U-Nets. Our model approximates three-dimensional, monochromatic radiative transfer in time-dependent regimes, in absorption-emission approximation, achieving speedups of more than 2 orders of magnitude while maintaining an average relative error below 3%, demonstrating our approach's potential to be integrated into state-of-the-art hydrodynamic simulations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。