In the late stages of planet formation, collisions between planetary-size 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. However, despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. To overcome this problem, we show that techniques from machine learning and uncertainty quantification are capable of predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. This work is based on a new set of 10,700  high-resolution smoothed-particle hydrodyamics (SPH) simulations of pairwise collisions between rotating, differentiated bodies.

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