Non-Intrusive Surrogate Modelling Using Sparse Random Features With Applications in Crashworthiness Analysis
MCML Authors
Felix Krahmer
Prof. Dr.
Principal Investigator
Anna Veselovska
Dr.
Abstract
Felix Krahmer
Prof. Dr.
Principal Investigator
Anna Veselovska
Dr.
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.
article HJK+25
International Journal for Uncertainty Quantification
15.4. Mar. 2025.Authors
M. Herold • J. S. Jehle • F. Krahmer • A. VeselovskaLinks
DOIResearch Area
BibTeXKey: HJK+25