Artificial neural networks in the calibration of nonlinear mechanical models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F16%3A00239626" target="_blank" >RIV/68407700:21110/16:00239626 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/doi:10.1016/j.advengsoft.2016.01.017" target="_blank" >http://dx.doi.org/doi:10.1016/j.advengsoft.2016.01.017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.advengsoft.2016.01.017" target="_blank" >10.1016/j.advengsoft.2016.01.017</a>

Result language

angličtina

Original language name

Artificial neural networks in the calibration of nonlinear mechanical models

Original language description

Rapid development in numerical modelling of materials and the complexity of new models increase quickly together with their computational demands. Despite the growing performance of modern computers and clusters, calibration of such models from noisy experimental data remains a nontrivial and often computationally intensive task. Layered neural networks provide a robust and efficient technique for overcoming the time-consuming simulations of calibrated models. The potential advantages of neural networks include simple implementation and high versatility in approximating nonlinear relationships. Therefore, there were several approaches proposed in literature for accelerating the calibration of nonlinear models by neural networks. This contribution reviews and compares three possible strategies based on approximating (i) the model response, (ii) the inverse relationship between the model response and its parameters and (iii) an error function quantifying how well the model fits the data. The advantages and drawbacks of particular strategies are demonstrated with the calibration of four parameters of an affinity hydration model from simulated data as well as from experimental measurements. The affinity hydration model is highly nonlinear but computationally cheap, thus allowing its calibration without any approximation and better quantification of results obtained by the examined calibration strategies. This paper can be viewed as a guide for engineers to help them develop an appropriate strategy for their particular calibration problems.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Advances in Engineering Software

ISSN

0965-9978

e-ISSN

—

Volume of the periodical

95

Issue of the periodical within the volume

May

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

68-81

UT code for WoS article

000371899900007

EID of the result in the Scopus database

2-s2.0-84959377812