270 citations · 709 across the 23 of their papers we have counts for
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astro-ph.IM2020★ 32 cited
Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
Abigail R. Azari, John B. Biersteker, Ryan M. Dewey +13
Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen…
astro-ph.EP2020★ 40 cited
Realistic On-the-fly Outcomes of Planetary Collisions II: Bringing Machine Learning to N-body Simulations
Alexandre Emsenhuber, Saverio Cambioni, Erik Asphaug +3
Terrestrial planet formation theory is at a bottleneck, with the growing realization that pairwise collisions are treated far too simply. Here, and in our companion paper (Cambioni…