用AI模型加速小行星表面温计算，效率提升十万倍
Deep operator neural network applied to efficient computation of asteroid surface temperature and the Yarkovsky effect
- 用DeepONet神经网络学习小行星表面温度分布规律
- 预测误差仅1%，计算速度比传统方法快5个数量级
- 适合需要快速分析大量小行星热效应的研究者
小行星表面温度分布对基于热物理特性的研究至关重要。虽然直接数值模拟能高保真地建模表面温度，但计算耗时长，尤其在需反复计算温度分布时更为显著。为此，本文采用深度算子神经网络（DeepONet），其具备高效计算与良好泛化能力。结果表明，训练后的网络平均预测误差约为1%，计算成本降低五个数量级，从而可在多维参数空间中实现热物理特性分析。作为初步应用，我们通过嵌入由DeepONet推导的瞬时雅科夫斯基效应的直接N体模拟，分析了小行星(3200) Phaethon和(89433) 2001 WM41的轨道演化，验证了该人工智能方法的有效性与高效性。
原文摘要 · Abstract (English)
Surface temperature distribution is crucial for thermal property-based studies about irregular asteroids in our Solar System. While direct numerical simulations could model surface temperatures with high fidelity, they often take a significant amount of computational time, especially for problems where temperature distributions are required to be repeatedly calculated. To this end, deep operator neural network (DeepONet) provides a powerful tool due to its high computational efficiency and generalization ability. In this work, we applied DeepONet to the modelling of asteroid surface temperatures. Results show that the trained network is able to predict temperature with an accuracy of ~1% on average, while the computational cost is five orders of magnitude lower, hence enabling thermal property analysis in a multidimensional parameter space. As a preliminary application, we analyzed the orbital evolution of asteroids through direct N-body simulations embedded with instantaneous Yarkovsky effect inferred by DeepONet-based thermophysical modelling.Taking asteroids (3200) Phaethon and (89433) 2001 WM41 as examples, we show the efficacy and efficiency of our AI-based approach.
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