Data-driven modeling and learning in science and engineering - 03/12/19
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Abstract |
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.
Le texte complet de cet article est disponible en PDF.Keywords : Data-driven science, Data-driven modeling, Artificial intelligence, Machine learning, Data-science, Big data
Plan
Vol 347 - N° 11
P. 845-855 - novembre 2019 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
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