Details
| Original language | English |
|---|---|
| Pages (from-to) | 4285-4318 |
| Number of pages | 34 |
| Journal | Archives of Computational Methods in Engineering |
| Volume | 29 |
| Issue number | 6 |
| Early online date | 7 May 2022 |
| Publication status | Published - Oct 2022 |
Abstract
The complexity of many problems in computational mechanics calls for reliable programming codes and accurate simulation systems. Typically, simulation responses strongly depend on material and model parameters, where one distinguishes between backward and forward models. Providing reliable information for the material/model parameters, enables us to calibrate the forward model (e.g., a system of PDEs). Markov chain Monte Carlo methods are efficient computational techniques to estimate the posterior density of the parameters. In the present study, we employ Bayesian inversion for several mechanical problems and study its applicability to enhance the model accuracy. Seven different boundary value problems in coupled multi-field (and multi-physics) systems are presented. To provide a comprehensive study, both rate-dependent and rate-independent equations are considered. Moreover, open source codes (https://doi.org/10.5281/zenodo.6451942) are provided, constituting a convenient platform for future developments for, e.g., multi-field coupled problems. The developed package is written in MATLAB and provides useful information about mechanical model problems and the backward Bayesian inversion setting.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Applied Mathematics
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In: Archives of Computational Methods in Engineering, Vol. 29, No. 6, 10.2022, p. 4285-4318.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Bayesian Inversion with Open-Source Codes for Various One-Dimensional Model Problems in Computational Mechanics
AU - Noii, Nima
AU - Khodadadian, Amirreza
AU - Ulloa, Jacinto
AU - Aldakheel, Fadi
AU - Wick, Thomas
AU - François, Stijn
AU - Wriggers, Peter
N1 - Funding Information: The corresponding author F. Aldakheel appreciates the scientific support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in the Priority Program SPP 2020 within its second funding phase. T. Wick and P. Wriggers were funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD, EXC 2122 (project number: 390833453).
PY - 2022/10
Y1 - 2022/10
N2 - The complexity of many problems in computational mechanics calls for reliable programming codes and accurate simulation systems. Typically, simulation responses strongly depend on material and model parameters, where one distinguishes between backward and forward models. Providing reliable information for the material/model parameters, enables us to calibrate the forward model (e.g., a system of PDEs). Markov chain Monte Carlo methods are efficient computational techniques to estimate the posterior density of the parameters. In the present study, we employ Bayesian inversion for several mechanical problems and study its applicability to enhance the model accuracy. Seven different boundary value problems in coupled multi-field (and multi-physics) systems are presented. To provide a comprehensive study, both rate-dependent and rate-independent equations are considered. Moreover, open source codes (https://doi.org/10.5281/zenodo.6451942) are provided, constituting a convenient platform for future developments for, e.g., multi-field coupled problems. The developed package is written in MATLAB and provides useful information about mechanical model problems and the backward Bayesian inversion setting.
AB - The complexity of many problems in computational mechanics calls for reliable programming codes and accurate simulation systems. Typically, simulation responses strongly depend on material and model parameters, where one distinguishes between backward and forward models. Providing reliable information for the material/model parameters, enables us to calibrate the forward model (e.g., a system of PDEs). Markov chain Monte Carlo methods are efficient computational techniques to estimate the posterior density of the parameters. In the present study, we employ Bayesian inversion for several mechanical problems and study its applicability to enhance the model accuracy. Seven different boundary value problems in coupled multi-field (and multi-physics) systems are presented. To provide a comprehensive study, both rate-dependent and rate-independent equations are considered. Moreover, open source codes (https://doi.org/10.5281/zenodo.6451942) are provided, constituting a convenient platform for future developments for, e.g., multi-field coupled problems. The developed package is written in MATLAB and provides useful information about mechanical model problems and the backward Bayesian inversion setting.
UR - http://www.scopus.com/inward/record.url?scp=85129484284&partnerID=8YFLogxK
U2 - 10.1007/s11831-022-09751-6
DO - 10.1007/s11831-022-09751-6
M3 - Article
AN - SCOPUS:85129484284
VL - 29
SP - 4285
EP - 4318
JO - Archives of Computational Methods in Engineering
JF - Archives of Computational Methods in Engineering
SN - 1134-3060
IS - 6
ER -