PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Modelling and Simulation of Microstructural Evolution for Extra-terrestrial Planetary Chemistry Mapping Using Artificial Intelligence – a NASA Mars Rover Study
Version 1
: Received: 22 July 2020 / Approved: 23 July 2020 / Online: 23 July 2020 (12:56:42 CEST)
How to cite:
Rafique, M.M.A. Modelling and Simulation of Microstructural Evolution for Extra-terrestrial Planetary Chemistry Mapping Using Artificial Intelligence – a NASA Mars Rover Study. Preprints2020, 2020070566. https://doi.org/10.20944/preprints202007.0566.v1
Rafique, M.M.A. Modelling and Simulation of Microstructural Evolution for Extra-terrestrial Planetary Chemistry Mapping Using Artificial Intelligence – a NASA Mars Rover Study. Preprints 2020, 2020070566. https://doi.org/10.20944/preprints202007.0566.v1
Rafique, M.M.A. Modelling and Simulation of Microstructural Evolution for Extra-terrestrial Planetary Chemistry Mapping Using Artificial Intelligence – a NASA Mars Rover Study. Preprints2020, 2020070566. https://doi.org/10.20944/preprints202007.0566.v1
APA Style
Rafique, M.M.A. (2020). Modelling and Simulation of Microstructural Evolution for Extra-terrestrial Planetary Chemistry Mapping Using Artificial Intelligence – a NASA Mars Rover Study. Preprints. https://doi.org/10.20944/preprints202007.0566.v1
Chicago/Turabian Style
Rafique, M.M.A. 2020 "Modelling and Simulation of Microstructural Evolution for Extra-terrestrial Planetary Chemistry Mapping Using Artificial Intelligence – a NASA Mars Rover Study" Preprints. https://doi.org/10.20944/preprints202007.0566.v1
Abstract
Development of rovers and development of infrastructure which enables them to probe other planets (such as Mars) have sparked a lot of interest recently specially with increasing public attention in Moon and Mars program by National Aeronautics and Space Administration. This is designed to be achieved by various means such as advanced spectroscopy and artificial intelligent techniques such as deep learning and transfer learning to enable the rover to not only map the surface of planet but to get a detailed information about its chemical makeup in layers beneath (deep learning) and in areas around point of observation (transfer learning). In this work, which is part of a proposal, later approach is explored. A systematic strategy is presented which make use of aforementioned techniques developed for metallic glass matrix composites as benchmark and helps develop algorithms for chemistry mapping of actual Martian surface on Perseverance Rover launching shortly.
Keywords
Convolution nets; back propagating algorithms; inorganic materials
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
