• français
    • English
    français
  • Login
Help
View Item 
  •   Home
  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)
  • View Item
  • Home
  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Data-driven modeling and learning in science and engineering

Article dans une revue avec comité de lecture
Author
MONTÁNS, Francisco J.
302798 Universidad Politécnica de Madrid [UPM]
ccCHINESTA SORIA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
GÓMEZ-BOMBARELLI, Rafael
301950 Massachusetts Institute of Technology [MIT]
KUTZ, J. Nathan
425933 University of Washington [Tacoma]

URI
http://hdl.handle.net/10985/19480
DOI
10.1016/j.crme.2019.11.009
Date
2019
Journal
Comptes Rendus Mécanique

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.

Files in this item

Name:
PIMM_CRM_2019_CHINESTA.pdf
Size:
1.148Mb
Format:
PDF
Description:
Article
View/Open

Collections

  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)

Related items

Showing items related by title, author, creator and subject.

  • Complex Algorithms for Data-Driven Model Learning in Science and Engineering 
    Article dans une revue avec comité de lecture
    MONTÁNS, Francisco Javier; GÓMEZ-BOMBARELLI, Rafael; KUTZ, Jose Nathan; ccCHINESTA SORIA, Francisco (Wiley, 2019)
    In their first centuries, scientific and engineering develop-ments were dominated by empirical understanding which encapsulated the first paradigm of scientific discovery. After the Renaissance, the scientific revolution ...
  • Complex Algorithms for Data-Driven Model Learning in Science and Engineering 
    Article dans une revue avec comité de lecture
    MONTÁNS, Francisco J.; GÓMEZ-BOMBARELLI, Rafael; KUTZ, J. Nathan; ccCHINESTA SORIA, Francisco (Hindawi Limited, 2019)
    no abstract
  • Crossing Scales: Data-Driven Determination of the Micro-scale Behavior of Polymers From Non-homogeneous Tests at the Continuum-Scale 
    Article dans une revue avec comité de lecture
    AMORES, Víctor J.; MONTÁNS, Francisco J.; ccCUETO, Elias; ccCHINESTA SORIA, Francisco (Frontiers Media SA, 2022-05)
    We propose an efficient method to determine the micro-structural entropic behavior of polymer chains directly from a sufficiently rich non-homogeneous experiment at the continuum scale. The procedure is developed in 2 ...
  • Shape parametrization of bio-mechanical finite element models based on medical images 
    Article dans une revue avec comité de lecture
    LAUZERAL, Nathan; BORZACCHIELLO, Domenico; KUGLER, Michaël; GEORGE, Daniel; RÉMOND, Yves; HOSTETTLER, Alexandre; ccCHINESTA SORIA, Francisco (Taylor & Francis, 2019)
    The main objective of this study is to combine the statistical shape analysis with a morphing procedure in order to generate shape-parametric finite element models of tissues and organs and to explore the reliability and ...
  • A model order reduction approach to create patient-specific mechanical models of human liver in computational medicine applications. 
    Article dans une revue avec comité de lecture
    LAUZERAL, Nathan; BORZACCHIELLO, Domenico; KUGLER, Michael; RÉMOND, Yves; GEORGE, Daniel; HOSTETTLER, Alexandre; ccCHINESTA SORIA, Francisco (Elsevier, 2019)
    Background and objective: This paper focuses on computer simulation aspects of Digital Twin models in the medical framework. In particular, it addresses the need of fast and accurate simulators for the mechanical response ...

Browse

All SAMCommunities & CollectionsAuthorsIssue DateCenter / InstitutionThis CollectionAuthorsIssue DateCenter / Institution

Newsletter

Latest newsletterPrevious newsletters

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales