| Kurzfassung
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In this work, we build surrogate models to approximate the deformation behavior of face-centered cubic crystalline structures under load, based on the continuum dislocation dynamics (CDD) simulation. The CDD simulation is a powerful tool for modeling the stress, strain, and evolution of dislocations in a material, but it is computationally expensive. Surrogate models provide approximations of the results at a much lower computational cost. We propose two approaches to building surrogate models that only require the simulation parameters as inputs and predict the sequences of stress, strain, and dislocation density. The approaches comprise the use of time-independent multi-target regression and recurrent neural networks. We demonstrate the effectiveness by providing an extensive study of different implementations of both approaches. We find that, based on our dataset, a gradient-boosted trees model making time-independent predictions performs best in general and provides insights into feature importance. The approach significantly reduces the computational cost while still producing accurate results.
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