A data generation approach for surrogate models for magneto‐static simulations

A data generation approach for surrogate models for magneto‐static simulations

In this paper, we propose an efficient numerical scheme for the prediction of the magnetic stray fields in two‐dimensional random microheterogeneous materials. Since data‐driven models require thousands of training datasets, Finite Element Method simulations appear to be too time consuming. Therefor...

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Veröffentlicht in:Proceedings in applied mathematics and mechanics 2023-11, Vol.23 (3), p.n/a
Hauptverfasser: Niekamp, Rainer, Niemann, Johanna, Reichel, Maximilian, Schröder, Jörg
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creator Niekamp, Rainer
Niemann, Johanna
Reichel, Maximilian
Schröder, Jörg
description In this paper, we propose an efficient numerical scheme for the prediction of the magnetic stray fields in two‐dimensional random microheterogeneous materials. Since data‐driven models require thousands of training datasets, Finite Element Method simulations appear to be too time consuming. Therefore, a stochastic model based on Brownian motion, which uses an efficient evaluation of stochastic transition matrices, is used as a Poisson solver to generate training data.
doi_str_mv 10.1002/pamm.202300119
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