mlsanityData to 14 Oct 2026

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

arXiv 2212.14507 ↗First posted 30 Dec 2022cs.LG cs.CE cs.NAcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 40 references that are in the map. First posted on arXiv 30 Dec 2022.

Builds on

14 of 40 references.

References · 14

  1. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  2. 2019Just Go with the Flow: Self-Supervised Scene Flow Estimationcited by 98
  3. 2019Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAScited by 83
  4. 2018Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantificationcited by 81
  5. 2020Self-Supervised Monocular Scene Flow Estimationcited by 64
  6. 2021Weakly Supervised Learning of Rigid 3D Scene Flowcited by 60
  7. 2020Sparse Polynomial Chaos Expansions: Literature Survey and Benchmarkcited by 44
  8. 2019A consensus-based global optimization method for high dimensional machine learning problemscited by 23
  9. 2021Generalization Bounds for Sparse Random Feature Expansionscited by 22
  10. 2018Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approachcited by 16
  11. 2021Consensus-Based Optimization Methods Converge Globallycited by 16
  12. 2020Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learningcited by 12
2 more
  1. 2021Trends in Consensus-based optimizationcited by 8
  2. 2021Consensus-based Optimization and Ensemble Kalman Inversion for Global Optimization Problems with Constraintscited by 3

Built on by

Not cited yet.