# Nonintrusive Uncertainty Quantification for automotive crash problems with VPS/Pamcrash

arXiv: https://arxiv.org/abs/2102.07673 · HTML: https://mlsanity.com/paper/2102.07673/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 89 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy](https://mlsanity.com/paper/2001.01958/) (2020)
- [Quantifying Uncertainties in Fault Slip Distribution during the T=ohoku Tsunami using Polynomial Chaos](https://mlsanity.com/paper/1607.07414/) (2016)

## Abstract (arXiv)

Uncertainty Quantification (UQ) is a key discipline for computational modeling of complex systems, enhancing reliability of engineering simulations. In crashworthiness, having an accurate assessment of the behavior of the model uncertainty allows reducing the number of prototypes and associated costs. Carrying out UQ in this framework is especially challenging because it requires highly expensive simulations. In this context, surrogate models (metamodels) allow drastically reducing the computational cost of Monte Carlo process. Different techniques to describe the metamodel are considered, Ordinary Kriging, Polynomial Response Surfaces and a novel strategy (based on Proper Generalized Decomposition) denoted by Separated Response Surface (SRS). A large number of uncertain input parameters may jeopardize the efficiency of the metamodels. Thus, previous to define a metamodel, kernel Principal Component Analysis (kPCA) is found to be effective to simplify the model outcome description. A benchmark crash test is used to show the efficiency of combining metamodels with kPCA.