Comptes Rendus
Data-driven modeling and learning in science and engineering
Comptes Rendus. Mécanique, Volume 347 (2019) no. 11, pp. 845-855.

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.

Published online:
DOI: 10.1016/j.crme.2019.11.009
Keywords: Data-driven science, Data-driven modeling, Artificial intelligence, Machine learning, Data-science, Big data

Francisco J. Montáns 1; Francisco Chinesta 2; Rafael Gómez-Bombarelli 3; J. Nathan Kutz 4

1 Escuela Técnica Superior de Ingenieros Aeronáuticos, Universidad Politécnica de Madrid, Plaza Cardenal Cisneros 3, 28045 Madrid, Spain
2 ESI Group Chair @ PIMM, Arts et Métiers ParisTech, 151, boulevard de l'Hôpital, 75013 Paris, France
3 Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
4 Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA
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     title = {Data-driven modeling and learning in science and engineering},
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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.

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