A graph database for feature characterization of dislocation networks

Katzer, Balduin; Betsche, Daniel; Böhm, Klemens; Weygand, Daniel; Schulz, Katrin

doi:10.1016/j.scriptamat.2023.115841

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…We assume that the experimental stress-strain data, which is conducted at a lower strain rate compared to CDD simulation, incorporate information about the stability of the network dislocation density, which is determined by the inverse surrogate model through the magnitude of the reaction constants. However, further analysis on the derivation of physical-based constitutive laws for dislocation network stability and evolution from DDD simulations is required for a better understanding of these network processes [59][60][61]. Summarizing, the prediction of simulation input parameters based on quite limited consideration of strain states still exhibits meaningful results and enables the derivation of input parameter sets for CDD simulations representing the experimental mechanical behavior and the microstructural evolution beyond the strain states, the inverse surrogate model is trained on.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%