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arXiv · 2212.14507

Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis

Maternus Herold·Anna Veselovska·Jonas Jehle·Felix Krahmer
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Abstract

Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthetic and real data obtained from crashworthiness analyses. The results show a superiority of the here described approach over state of the art surrogate modelling techniques, Polynomial Chaos Expansions and Neural Networks.

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Maternus Herold, Anna Veselovska, Jonas Jehle, Felix Krahmer. 2022-12-30. Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis. https://arxiv.org/abs/2212.14507

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