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

• URL: http://arxiv.org/abs/2212.14507v1
• Date: Fri, 30 Dec 2022 01:29:21 GMT
• Title: Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis
• Authors: Maternus Herold, Anna Veselovska, Jonas Jehle, and Felix Krahmer
• Abstract summary: A novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The results show a superiority of the here described approach over state of the art surrogate modelling techniques, Polynomial Chaos Expansions and Neural Networks.
• Score: 4.521832548328702
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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