Uncertainty quantification patterns for multiscale models

被引:8
|
作者
Ye, D. [1 ]
Veen, L. [2 ]
Nikishova, A. [1 ]
Lakhlili, J. [3 ]
Edeling, W. [4 ]
Luk, O. O. [3 ]
Krzhizhanovskaya, V. V. [1 ,5 ]
Hoekstra, A. G. [1 ]
机构
[1] Univ Amsterdam, Fac Sci, Informat Inst, Computat Sci Lab, Amsterdam, Netherlands
[2] Netherlands eSci Ctr, Amsterdam, Netherlands
[3] Max Planck Inst Plasma Phys, Garching, Germany
[4] Ctr Wiskunde & Informat, Sci Comp Grp, Amsterdam, Netherlands
[5] ITMO Univ, St Petersburg, Russia
关键词
uncertainty quantification; uncertainty propagation; multiscale simulation; surrogate modelling; PROPAGATION;
D O I
10.1098/rsta.2020.0072
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Uncertainty quantification (UQ) is a key component when using computational models that involve uncertainties, e.g. in decision-making scenarios. In this work, we present uncertainty quantification patterns (UQPs) that are designed to support the analysis of uncertainty in coupled multi-scale and multi-domain applications. UQPs provide the basic building blocks to create tailored UQ for multiscale models. The UQPs are implemented as generic templates, which can then be customized and aggregated to create a dedicated UQ procedure for multiscale applications. We present the implementation of the UQPs with multiscale coupling toolkit Multiscale Coupling Library and Environment 3. Potential speed-up for UQPs has been derived as well. As a proof of concept, two examples of multiscale applications using UQPs are presented. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.
引用
下载
收藏
页数:17
相关论文
共 50 条
  • [41] Uncertainty quantification in the parameters of soil constitutive models
    Xue, Yang
    Miao, Fa-Sheng
    Wu, Yi-Ping
    Wen, Tao
    Wang, Yan-Kun
    Yantu Lixue/Rock and Soil Mechanics, 2024, 45 (09): : 2797 - 2807
  • [42] Uncertainty quantification of multispecies droplet evaporation models
    Lupo, Giandomenico
    Duwig, Christophe
    INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2020, 154
  • [43] An uncertainty quantification method for nanomaterial prediction models
    O. Arda Vanli
    Li-Jen Chen
    Chao-his Tsai
    Chuck Zhang
    Ben Wang
    The International Journal of Advanced Manufacturing Technology, 2014, 70 : 33 - 44
  • [44] Variational inference: uncertainty quantification in additive models
    Lichter, Jens
    Wiemann, Paul F., V
    Kneib, Thomas
    ASTA-ADVANCES IN STATISTICAL ANALYSIS, 2024, 108 (02) : 279 - 331
  • [45] Uncertainty quantification of 2 models of cardiac electromechanics
    Hurtado, Daniel E.
    Castro, Sebastian
    Madrid, Pedro
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, 2017, 33 (12)
  • [46] An uncertainty quantification method for nanomaterial prediction models
    Vanli, O. Arda
    Chen, Li-Jen
    Tsai, Chao-his
    Zhang, Chuck
    Wang, Ben
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2014, 70 (1-4): : 33 - 44
  • [47] RANDOM PREDICTOR MODELS FOR RIGOROUS UNCERTAINTY QUANTIFICATION
    Crespo, Luis G.
    Kenny, Sean P.
    Giesy, Daniel P.
    INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2015, 5 (05) : 469 - 489
  • [48] Methods for the Uncertainty Quantification of Aircraft Simulation Models
    Rosic, Bojana V.
    Diekmann, Jobst H.
    JOURNAL OF AIRCRAFT, 2015, 52 (04): : 1247 - 1255
  • [49] On the uncertainty quantification of blood flow viscosity models
    Pereira, J. M. C.
    Serra e Moura, J. P.
    Ervilha, A. R.
    Pereira, J. C. F.
    CHEMICAL ENGINEERING SCIENCE, 2013, 101 : 253 - 265
  • [50] Uncertainty quantification in continuous fragmentation airburst models
    McMullan, S.
    Collins, G. S.
    ICARUS, 2019, 327 : 19 - 35