Comptes Rendus Mécanique (CRM)

Pierre Collet

A short historical account of period doublings in the pre-renormalization era

Stéphan Fauve

Gérard Iooss

Quasipatterns versus superlattices resulting from the superposition of two hexagonal patterns

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Editorial board:
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Editorial Board

Francesco dell'Isola

Emilio Turco

Anil Misra

Zacharias Vangelatos

Costas Grigoropoulos

Vasileia Melissinaki

Maria Farsari

Force–displacement relationship in micro-metric pantographs: Experiments and numerical simulations

Francisco Chinesta

Elias Cueto

Pierre Ladevèze

C. R. Mecanique 347 (2019) 741Contents lists available at ScienceDirect
Comptes Rendus Mecanique
www.sciencedirect.com
Data-Based Engineering Science and Technology / Sciences et technologies de l’ingénierie basées sur les données
Foreword
Engineering sciences and technology, as any other branch of sciences and technology, are experiencing the data revolu-
tion. In the past, models were more abundant than data, too expensive to be collected, and analyzed at that time. However, 
nowadays, the situation is radically different, data is much more abundant (and sometimes more accurate) than existing 
models, and a new paradigm is emerging in engineering sciences and technology.
Advanced clustering techniques not only help engineers and analysts; they become crucial in many areas where models, 
approximation bases, parameters. . . are adapted depending on the local (in space and time senses) state of the system.
Machine learning is also helping for extracting the manifold in which the solutions to complex and coupled engineer-
ing problems are living. Thus, uncorrelated parameters can be efficiently extracted from the collected data coming from 
numerical simulations, experiments, or even from the data collected from adequate measurement devices.
Model learners, deep learning, and other techniques based on manifold or dictionary learning, tensor formats, sparse 
sensing, advanced nonlinear regression, and DMD, among many others, are capturing the interest of the applied mathematics 
community.
Sampling issues, addressing noise and uncertainty, are crucial to move from the nowadays big-data framework to the 
incipient smart-data paradigm.
Thus, the subtle circle is closed by linking data to information, information to knowledge, and finally knowledge to 
real-time decision-making, opening unimaginable possibilities within the so-called DDDAS (Dynamic Data Driven Application 
Systems).
Francisco Chinesta ∗
“Arts et Métiers” Institute of Technology, Paris, France
E-mail address: francisco.chinesta@ensam.eu
Elias Cueto
University of Zaragoza, Spain
Pierre Ladevèze
ENS Paris-Saclay, France
Available online 14 November 2019* Corresponding author.
https://doi.org/10.1016/j.crme.2019.11.016
1631-0721/© 2019 Académie des sciences. Published by Elsevier Masson SAS. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/by-nc-nd/4.0/).


Foreword

David Néron

Paul-William Gerbaud

Data-driven computation for history-dependent materials

Enrique Nadal

David Muñoz

Nieves Vivó

Irene Lucas

Juan José Ródenas

Evaluation of hip fracture risk using a hyper-parametric model based on the Locally Linear Embedding technique

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A short historical account of period doublings in the pre-renormalization era

Quasipatterns versus superlattices resulting from the superposition of two hexagonal patterns

Editorial Board

Force–displacement relationship in micro-metric pantographs: Experiments and numerical simulations

Editorial Board

Editorial Board

Editorial Board

Foreword

Data-driven computation for history-dependent materials

Evaluation of hip fracture risk using a hyper-parametric model based on the Locally Linear Embedding technique

2,303

2,511