用Transformer模型加速行星大气辐射传输计算，速度提升100倍且误差仅1%。
Accelerating Radiative Transfer for Planetary Atmospheres by Orders of Magnitude with a Transformer-Based Machine Learning Model
- 采用仅编码器的Transformer架构，学习大气剖面到辐射通量的映射关系。
- 在热木星大气数据上测试，平均误差约1%，相比传统方法提速100倍。
- 适合需要高精度快速模拟的气候模型研究者，尤其适用于大尺度行星模拟。
辐射传输计算对行星大气建模至关重要，但传统方法计算成本高且需在精度与速度间权衡。高计算开销迫使大型模型（如全球环流模型，GCM）进行数值简化，从而降低模拟精度。辐射传输非常适合机器学习替代：其本质是静态大气剖面到辐射通量的确定性物理映射，且可通过第一性原理计算生成高保真训练数据。本文采用仅编码器的Transformer神经网络架构，基于太阳成分热木星的一维大气剖面进行训练。该模型在双流层总辐射通量上实现了与传统方法相比约1%的平均测试误差，并获得100倍的速度提升。利用机器学习模拟辐射传输，为行星大气模型（如GCM）提供了更快、更精确的计算可能。
原文摘要 · Abstract (English)
Radiative transfer calculations are essential for modeling planetary atmospheres. However, standard methods are computationally demanding and impose accuracy-speed trade-offs. High computational costs force numerical simplifications in large models (e.g., General Circulation Models) that degrade the accuracy of the simulation. Radiative transfer calculations are an ideal candidate for machine learning emulation: fundamentally, it is a well-defined physical mapping from a static atmospheric profile to the resulting fluxes, and high-fidelity training data can be created from first principles calculations. We developed a radiative transfer emulator using an encoder-only transformer neural network architecture, trained on 1D profiles representative of solar-composition hot Jupiter atmospheres. Our emulator reproduced bolometric two-stream layer fluxes with mean test set errors of ~1% compared to the traditional method and achieved speedups of 100x. Emulating radiative transfer with machine learning opens up the possibility for faster and more accurate routines within planetary atmospheric models such as GCMs.
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