用神经网络加速彗星热物理建模，600万倍提速且误差仅2%
ThermoONet -- a deep learning-based small body thermophysical network: applications to modelling water activity of comets
- 用深度学习构建彗星温度与冰升华通量预测网络
- 计算速度提升近六阶，地表下温度误差约2%
- 可高效反演彗星物理属性，适合高精度探测研究
彗星活动是重要研究课题，热物理模型在其中起关键作用。传统数值方法计算成本高，难以支持高分辨率或重复模拟。为此，我们采用机器学习方法开发了ThermoONet——一个用于预测彗星温度和水冰升华通量的神经网络。性能评估显示，该网络对地表下温度的平均误差约为2%，计算时间减少近六个数量级。我们将ThermoONet应用于67P/Churyumov-Gerasimenko和21P/Giacobini-Zinner彗星，成功拟合了罗塞塔任务和SOHO望远镜获取的水生成率曲线，验证了其有效性和高效性。此外，结合全局优化算法，ThermoONet能有效反演出目标天体的物理特性。
原文摘要 · Abstract (English)
Cometary activity is a compelling subject of study, with thermophysical models playing a pivotal role in its understanding. However, traditional numerical solutions for small body thermophysical models are computationally intensive, posing challenges for investigations requiring high-resolution or repetitive modeling. To address this limitation, we employed a machine learning approach to develop ThermoONet - a neural network designed to predict the temperature and water ice sublimation flux of comets. Performance evaluations indicate that ThermoONet achieves a low average error in subsurface temperature of approximately 2% relative to the numerical simulation, while reducing computational time by nearly six orders of magnitude. We applied ThermoONet to model the water activity of comets 67P/Churyumov-Gerasimenko and 21P/Giacobini-Zinner. By successfully fitting the water production rate curves of these comets, as obtained by the Rosetta mission and the SOHO telescope, respectively, we demonstrate the network's effectiveness and efficiency. Furthermore, when combined with a global optimization algorithm, ThermoONet proves capable of retrieving the physical properties of target bodies.
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