Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data

被引:161
作者
Chinesta, Francisco [1 ]
Cueto, Elias [2 ]
Abisset-Chavanne, Emmanuelle [3 ]
Duval, Jean Louis [4 ]
Khaldi, Fouad El [4 ]
机构
[1] ENSAM ParisTech, 151 Blvd Hop, F-75013 Paris, France
[2] Univ Zaragoza, Aragon Inst Engn Res, Maria de Luna S-N, Zaragoza 50018, Spain
[3] Ecole Cent Nantes, ESI Grp Chair, 1 Rue Noe, F-44300 Nantes, France
[4] ESI Grp, 3Bis Rue Saarinen, F-94528 Rungis, France
关键词
MATERIAL PARAMETER-IDENTIFICATION; MODEL ORDER REDUCTION; NONLINEAR DIMENSIONALITY REDUCTION; CONSISTENT CLUSTERING ANALYSIS; COMPUTATIONAL HOMOGENIZATION; WYPIWYG HYPERELASTICITY; VISCOPLASTIC MODELS; DATA ASSIMILATION; DECOMPOSITION; THERMODYNAMICS;
D O I
10.1007/s11831-018-9301-4
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Engineering is evolving in the same way than society is doing. Nowadays, data is acquiring a prominence never imagined. In the past, in the domain of materials, processes and structures, testing machines allowed extract data that served in turn to calibrate state-of-the-art models. Some calibration procedures were even integrated within these testing machines. Thus, once the model had been calibrated, computer simulation takes place. However, data can offer much more than a simple state-of-the-art model calibration, and not only from its simple statistical analysis, but from the modeling and simulation viewpoints. This gives rise to the the family of so-called twins: the virtual, the digital and the hybrid twins. Moreover, as discussed in the present paper, not only data serve to enrich physically-based models. These could allow us to perform a tremendous leap forward, by replacing big-data-based habits by the incipient smart-data paradigm.
引用
收藏
页码:105 / 134
页数:30
相关论文
共 107 条
  • [1] Aguado JV, 2018, J SCI COMPUT
  • [2] Anandkumar A, 2014, J MACH LEARN RES, V15, P2773
  • [3] [Anonymous], 2017, Model Reduction Methods
  • [4] [Anonymous], TR2011114
  • [5] A new reliability-based data-driven approach for noisy experimental data with physical constraints
    Ayensa-Jimenez, Jacobo
    Doweidar, Mohamed H.
    Sanz-Herrera, Jose A.
    Doblare, Manuel
    [J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2018, 328 : 752 - 774
  • [6] Reduced order modeling for physically-based augmented reality
    Badias, Alberto
    Alfaro, Iciar
    Gonzalez, David
    Chinesta, Francisco
    Cueto, Elias
    [J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2018, 341 : 53 - 70
  • [7] An 'empirical interpolation' method: application to efficient reduced-basis discretization of partial differential equations
    Barrault, M
    Maday, Y
    Nguyen, NC
    Patera, AT
    [J]. COMPTES RENDUS MATHEMATIQUE, 2004, 339 (09) : 667 - 672
  • [8] A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality
    Bessa, M. A.
    Bostanabad, R.
    Liu, Z.
    Hu, A.
    Apley, Daniel W.
    Brinson, C.
    Chen, W.
    Liu, Wing Kam
    [J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2017, 320 : 633 - 667
  • [9] Advanced simulation of models defined in plate geometries: 3D solutions with 2D computational complexity
    Bognet, B.
    Bordeu, F.
    Chinesta, F.
    Leygue, A.
    Poitou, A.
    [J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2012, 201 : 1 - 12
  • [10] Bognet B, 2014, ADV MODEL SIMUL ENG, V1, P4