Exploiting Machine Learning in Multiscale Modelling of Materials

Anand, G.; Ghosh, Swarnava; Zhang, Liwei; Anupam, Angesh; Freeman, Colin L.; Ortner, Christoph; Eisenbach, Markus; Kermode, James R.

doi:10.1007/s40033-022-00424-z

Cited by 5 publications

(1 citation statement)

References 101 publications

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“…Forward surrogate models are applicable, for example, in multiscale materials modelling to inform models on a larger length scale by surrogate models based on a smaller length scale [22,23]. Fernandez et al [24] apply an artificial neural network (ANN) surrogate to model the constitutive behavior of grain boundaries by using a MD database.…”

Section: Introductionmentioning

confidence: 99%

Combining simulation and experimental data via surrogate modelling of continuum dislocation dynamics simulations

Katzer,

Betsche,

von Hoegen

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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Several computational models have been introduced in recent years toyield comprehensive insights into microstructural evolution analyses. However, theidentification of the correct input parameters to a simulation that corresponds to acertain experimental result is a major challenge on this length scale. To complementsimulation results with experimental data (and vice versa) is not trivial since, e.g.,simulation model parameters might lack a physical understanding or uncertaintiesin the experimental data are neglected. Computational costs are another challengemesoscale models always have to face, so comprehensive parameter studies can becostly. In this paper, we introduce a surrogate model to circumvent continuumdislocation dynamics simulation by a data-driven linkage between well-defined inputparameters and output data and vice versa. We present meaningful results for aforward surrogate formulation that predicts simulation output based on the inputparameter space, as well as for the inverse approach that derives the input parameterspace based on simulation as well as experimental output quantities. This enables, e.g.,a direct derivation of the input parameter space of a continuum dislocation dynamicssimulation based on experimentally provided stress-strain data.

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Section: Introductionmentioning

confidence: 99%

Combining simulation and experimental data via surrogate modelling of continuum dislocation dynamics simulations

Katzer,

Betsche,

von Hoegen

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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Can neural networks estimate parameters in epidemiology models using real observed data?

Ahmad,

Günel

2024

Appl Intell

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Exploiting Machine Learning in Multiscale Modelling of Materials

Anand

Ghosh

Zhang

et al. 2022

J. Inst. Eng. India Ser. D

Self Cite

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of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed.This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (http://energy.gov/downloads/doe-publicaccess-plan).

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Exploiting Machine Learning in Multiscale Modelling of Materials

Cited by 5 publications

References 101 publications

Combining simulation and experimental data via surrogate modelling of continuum dislocation dynamics simulations

Combining simulation and experimental data via surrogate modelling of continuum dislocation dynamics simulations

Can neural networks estimate parameters in epidemiology models using real observed data?

Exploiting Machine Learning in Multiscale Modelling of Materials

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