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A machine-learning-based surrogate model of Mars' thermal evolution

Agarwal, Siddhant and Tosi, Nicola and Breuer, Doris and Padovan, Sebastiano and Kessel, Pan and Montavon, Grégoire (2020) A machine-learning-based surrogate model of Mars' thermal evolution. Geophysical Journal International, 222 (3), pp. 1656-1670. Oxford University Press. DOI: 10.1093/gji/ggaa234 ISSN 0956-540X

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Official URL: https://academic.oup.com/gji/article/222/3/1656/5836720

Abstract

Constraining initial conditions and parameters of mantle convection for a planet often requires running several hundred computationally expensive simulations in order to find those matching certain ‘observables’, such as crustal thickness, duration of volcanism, or radial contraction. A lower fidelity alternative is to use 1-D evolution models based on scaling laws that parametrize convective heat transfer. However, this approach is often limited in the amount of physics that scaling laws can accurately represent (e.g. temperature and pressure-dependent rheologies or mineralogical phase transitions can only be marginally simulated). We leverage neural networks to build a surrogate model that can predict the entire evolution (0–4.5 Gyr) of the 1-D temperature profile of a Mars-like planet for a wide range of values of five different parameters: reference viscosity, activation energy and activation volume of diffusion creep, enrichment factor of heat-producing elements in the crust and initial temperature of the mantle. The neural network we evaluate and present here has been trained from a subset of ∼10 000 evolution simulations of Mars ran on a 2-D quarter-cylindrical grid, from which we extracted laterally averaged 1-D temperature profiles. The temperature profiles predicted by this trained network match those of an unseen batch of 2-D simulations with an average accuracy of 99.7per cent⁠.

Item URL in elib:https://elib.dlr.de/136662/
Document Type:Article
Title:A machine-learning-based surrogate model of Mars' thermal evolution
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Agarwal, SiddhantSiddhant.Agarwal (at) dlr.dehttps://orcid.org/0000-0002-0840-2114
Tosi, Nicolanicola.tosi (at) dlr.dehttps://orcid.org/0000-0002-4912-2848
Breuer, DorisDoris.Breuer (at) dlr.dehttps://orcid.org/0000-0001-9019-5304
Padovan, SebastianoSebastiano.Padovan (at) dlr.dehttps://orcid.org/0000-0002-8652-3704
Kessel, Panpan.kessel (at) campus.tu-berlin.deUNSPECIFIED
Montavon, Grégoiregregoire.montavon (at) tu-berlin.deUNSPECIFIED
Date:13 May 2020
Journal or Publication Title:Geophysical Journal International
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:222
DOI :10.1093/gji/ggaa234
Page Range:pp. 1656-1670
Publisher:Oxford University Press
ISSN:0956-540X
Status:Published
Keywords:Mantle processes, Neural networks, fuzzy logic, Planetary interiors
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Science and Exploration
DLR - Research area:Raumfahrt
DLR - Program:R EW - Erforschung des Weltraums
DLR - Research theme (Project):R - Vorhaben Exploration des Sonnensystems
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Planetary Research
Institute of Planetary Research > Planetary Physics
Deposited By: Agarwal, Siddhant
Deposited On:16 Oct 2020 08:20
Last Modified:16 Oct 2020 08:20

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