Residual neural networks for the prediction of planetary collision outcomes. Issue 1 (14th October 2022)
- Record Type:
- Journal Article
- Title:
- Residual neural networks for the prediction of planetary collision outcomes. Issue 1 (14th October 2022)
- Main Title:
- Residual neural networks for the prediction of planetary collision outcomes
- Authors:
- Winter, Philip M
Burger, Christoph
Lehner, Sebastian
Kofler, Johannes
Maindl, Thomas I
Schäfer, Christoph M - Abstract:
- ABSTRACT: Fast and accurate treatment of collisions in the context of modern N -body planet formation simulations remains a challenging task due to inherently complex collision processes. We aim to tackle this problem with machine learning (ML), in particular via residual neural networks. Our model is motivated by the underlying physical processes of the data-generating process and allows for flexible prediction of post-collision states. We demonstrate that our model outperforms commonly used collision handling methods such as perfect inelastic merging and feed-forward neural networks in both prediction accuracy and out-of-distribution generalization. Our model outperforms the current state of the art in 20/24 experiments. We provide a data set that consists of 10164 Smooth Particle Hydrodynamics (SPH) simulations of pairwise planetary collisions. The data set is specifically suited for ML research to improve computational aspects for collision treatment and for studying planetary collisions in general. We formulate the ML task as a multi-task regression problem, allowing simple, yet efficient training of ML models for collision treatment in an end-to-end manner. Our models can be easily integrated into existing N -body frameworks and can be used within our chosen parameter space of initial conditions, i.e. where similar-sized collisions during late-stage terrestrial planet formation typically occur.
- Is Part Of:
- Monthly notices of the Royal Astronomical Society. Volume 520:Issue 1(2023)
- Journal:
- Monthly notices of the Royal Astronomical Society
- Issue:
- Volume 520:Issue 1(2023)
- Issue Display:
- Volume 520, Issue 1 (2023)
- Year:
- 2023
- Volume:
- 520
- Issue:
- 1
- Issue Sort Value:
- 2023-0520-0001-0000
- Page Start:
- 1224
- Page End:
- 1242
- Publication Date:
- 2022-10-14
- Subjects:
- hydrodynamics -- methods: numerical -- astronomical data bases: miscellaneous -- celestial mechanics -- planets and satellites: composition -- planets and satellites: formation
Astronomy -- Periodicals
Periodicals
520.5 - Journal URLs:
- http://mnras.oxfordjournals.org/ ↗
http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-2966 ↗
http://www.blackwell-synergy.com/issuelist.asp?journal=mnr ↗
http://www.blackwell-synergy.com/loi/mnr ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/mnras/stac2933 ↗
- Languages:
- English
- ISSNs:
- 0035-8711
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5943.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25685.xml