Machine learning applied to simulations of collisions between rotating, differentiated planets.

ABSTRACT: 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 predict

SUBMITTER: Timpe ML 

PROVIDER: S-EPMC7716936 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature