In the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to either growth or disruption of the bodies involved and are largely responsible for shaping the final characteristics of the planets. Despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. While semi-analytic methods have been proposed, they remain limited to a narrow set of post-impact properties and have only achieved relatively low accuracies. However, the rise of machine learning and access to increased computing power have enabled novel data-driven approaches. In this work, we show that data-driven emulation techniques are capable of classifying and predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. In particular, we focus on the dataset requirements, training pipeline, and classification and regression performance for four distinct data-driven techniques from machine learning (ensemble methods and neural networks) and uncertainty quantification (Gaussian processes and polynomial chaos expansion). We compare these methods to existing analytic and semi-analytic methods. Such data-driven emulators are poised to replace the methods currently used in N-body simulations, while avoiding the cost of direct simulation. This work is based on a new set of 14,856 SPH simulations of pairwise collisions between rotating, differentiated bodies at all possible mutual orientations.

中文翻译：

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机器学习应用于模拟旋转、分化行星之间的碰撞

在类地行星形成的后期，行星大小的天体之间的成对碰撞是行星生长的基本因素。这些碰撞可能导致相关物体的生长或破坏，并且在很大程度上决定了行星的最终特征。尽管它们在行星形成中发挥着关键作用，但尚未实现对碰撞的准确处理。虽然已经提出了半分析方法，但它们仍然局限于一组狭窄的冲击后特性，并且只能达到相对较低的准确度。然而，机器学习的兴起和对更高计算能力的访问使得新的数据驱动方法成为可能。在这项工作中，我们表明，数据驱动的仿真技术能够以高精度对碰撞结果进行分类和预测，并且可以推广到任何可量化的碰撞后量。特别是，我们关注来自机器学习（集成方法和神经网络）和不确定性量化（高斯过程和多项式混沌扩展）的四种不同数据驱动技术的数据集要求、训练管道以及分类和回归性能。我们将这些方法与现有的分析和半分析方法进行比较。这种数据驱动的仿真器有望取代目前在 N 体仿真中使用的方法，同时避免直接仿真的成本。这项工作基于一组新的 14,856 个 SPH 模拟旋转之间的成对碰撞，