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Deep learning for surrogate modelling of 2D mantle convection

Agarwal, Siddhant and Tosi, Nicola and Kessel, P and Breuer, Doris and Montavon, Grégoire (2021) Deep learning for surrogate modelling of 2D mantle convection. 74th Annual Meeting of the American Physical Society’s Division of Fluid Dynamics, 21-22 Nov 2021, Online.

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Official URL: https://meetings.aps.org/Meeting/DFD20/Session/R01.9

Abstract

Exploring the high-dimensional parameter space governing 2D or 3D mantle convection simulations of terrestrial planets is computationally challenging. Hence, surrogates are helpful. Using 10,500 simulations of Mars’ thermal evolution carried out in a 2D cylindrical-shell geometry, we demonstrated that feedforward neural networks (FNN) can take five key parameters (initial temperature, radial distribution of radiogenic elements, reference viscosity, pressure- and temperature-dependence of the viscosity) plus time as an additional variable, and predict the 1D horizontally-averaged temperature profile at any time during 4.5 billion years of evolution (Agarwal et al. 2020). We now extend this work to predict the entire 2D temperature field which contains more information than the 1D profile such as the structure of plumes and downwellings. First, we compress the temperature fields by a factor of ~140 using a convolutional autoencoder. Then, we compare the use of FNN and long-short term memory networks (LSTM) for predicting this compressed state. While FNN predictions are slightly more accurate, LSTMs ultimately capture the flow dynamics significantly better. The entire spatio-temporal evolution of the temperature field can thus be predicted for a wide range of parameters.

Item URL in elib:https://elib.dlr.de/146292/
Document Type:Conference or Workshop Item (Speech)
Title:Deep learning for surrogate modelling of 2D mantle convection
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
Kessel, PTechnical University BerlinUNSPECIFIED
Breuer, DorisDoris.Breuer (at) dlr.dehttps://orcid.org/0000-0001-9019-5304
Montavon, GrégoireInstitut für Softwaretechnik und Theoretische Informatik, Technische Universität BerlinUNSPECIFIED
Date:November 2021
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Mantle Convection, Machine Learning, Fluid Dynamics, Surrogate Modelling, Neural Networks
Event Title:74th Annual Meeting of the American Physical Society’s Division of Fluid Dynamics
Event Location:Online
Event Type:international Conference
Event Dates:21-22 Nov 2021
Organizer:American Physical Society
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Exploration
DLR - Research area:Raumfahrt
DLR - Program:R EW - Space Exploration
DLR - Research theme (Project):R - Exploration of the Solar System
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Planetary Research > Planetary Physics
Deposited By: Agarwal, Siddhant
Deposited On:26 Nov 2021 12:45
Last Modified:26 Nov 2021 12:45

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  • Deep learning for surrogate modelling of 2D mantle convection. (deposited 26 Nov 2021 12:45) [Currently Displayed]

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