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

12/30/2022
by   Maternus Herold, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset