Unravelling interior evolution of terrestrial planets using machine learning.

Agarwal, Siddhant and Tosi, Nicola and Breuer, Doris and Kessel, Pan and Montavon, Grégoire (2019) Unravelling interior evolution of terrestrial planets using machine learning. Artificial Intelligence in Astronom, 2019, Munich, Germany.

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Official URL: https://www.eso.org/sci/meetings/2019/AIA2019/PDF/Contributed/Agarwal.pdf

Item URL in elib:

https://elib.dlr.de/136680/

Document Type:

Conference or Workshop Item (Speech)

Title:

Unravelling interior evolution of terrestrial planets using machine learning.

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD
Agarwal, Siddhant	UNSPECIFIED	https://orcid.org/0000-0002-0840-2114
Tosi, Nicola	UNSPECIFIED	https://orcid.org/0000-0002-4912-2848
Breuer, Doris	UNSPECIFIED	https://orcid.org/0000-0001-9019-5304
Kessel, Pan	UNSPECIFIED	UNSPECIFIED
Montavon, Grégoire	UNSPECIFIED	UNSPECIFIED

Date:

July 2019

Refereed publication:

Open Access:

Yes

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Status:

Published

Keywords:

Inverse problem, Mixture Density Networks, Thermal Evolution, Mars, Mantle Convection

Event Title:

Artificial Intelligence in Astronom

Event Location:

Munich, Germany

Event Type:

international Conference

Event Dates:

2019

Organizer:

European Southern Observatory

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
Institute of Planetary Research > Planetary Physics

Deposited By:

Agarwal, Siddhant

Deposited On:

16 Oct 2020 08:18

Last Modified:

16 Oct 2020 08:18

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