In the past, data in which science and engineering is based, was scarce and frequently obtained by experiments proposed to verify a given hypothesis. Each experiment was able to yield only very limited data. Today, data is abundant and abundantly collected in each single experiment at a very small cost. Data-driven modeling and scientific discovery is a change of paradigm on how many problems, both in science and engineering, are addressed. Some scientific fields have been using artificial intelligence for some time due to the inherent difficulty in obtaining laws and equations to describe some phenomena. However, today data-driven approaches are also flooding fields like mechanics and materials science, where the traditional approach seemed to be highly satisfactory. In this paper we review the application of data-driven modeling and model learning procedures to different fields in science and engineering.
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@article{CRMECA_2019__347_11_845_0,
author = {Francisco J. Mont\'ans and Francisco Chinesta and Rafael G\'omez-Bombarelli and J. Nathan Kutz},
title = {Data-driven modeling and learning in science and engineering},
journal = {Comptes Rendus. M\'ecanique},
pages = {845--855},
publisher = {Elsevier},
volume = {347},
number = {11},
year = {2019},
doi = {10.1016/j.crme.2019.11.009},
language = {en},
}
TY - JOUR AU - Francisco J. Montáns AU - Francisco Chinesta AU - Rafael Gómez-Bombarelli AU - J. Nathan Kutz TI - Data-driven modeling and learning in science and engineering JO - Comptes Rendus. Mécanique PY - 2019 SP - 845 EP - 855 VL - 347 IS - 11 PB - Elsevier DO - 10.1016/j.crme.2019.11.009 LA - en ID - CRMECA_2019__347_11_845_0 ER -
%0 Journal Article %A Francisco J. Montáns %A Francisco Chinesta %A Rafael Gómez-Bombarelli %A J. Nathan Kutz %T Data-driven modeling and learning in science and engineering %J Comptes Rendus. Mécanique %D 2019 %P 845-855 %V 347 %N 11 %I Elsevier %R 10.1016/j.crme.2019.11.009 %G en %F CRMECA_2019__347_11_845_0
Francisco J. Montáns; Francisco Chinesta; Rafael Gómez-Bombarelli; J. Nathan Kutz. Data-driven modeling and learning in science and engineering. Comptes Rendus. Mécanique, Volume 347 (2019) no. 11, pp. 845-855. doi : 10.1016/j.crme.2019.11.009. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2019.11.009/
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