Crash simulation — a car body folding, FEM mesh visible, stress blooming ember at the crease

Case 01 · crash-ai

Outliers in the Crash

ANOMALY DETECTION · 3D · UNSUPERVISED

2022 — present · Kube GmbH · for Porsche AG

Large-scale crash studies produce thousands of finite-element simulations. Monitoring them by hand is slow and inconsistent; the dangerous case is the one nobody noticed.

Close view of a crash fold line, wireframe over sheet metal, ember fracture light

I built methods that learn a low-dimensional representation of each simulation and detect anomalous deformation behaviour automatically — geometric deep learning on the mesh, anomaly scoring on the embeddings.

The work became a method, then a paper, then production tooling that surfaces the simulation an engineer needs to look at.

apparatus

  • PyTorch
  • Geometric Deep Learning
  • PointNet++
  • Autoencoders

scale

10³+ sims / study

published

International Journal of Crashworthiness, 2022

Method for Automated Detection of Outliers in Crash Simulations · with Dr. David Kracker

Read the paper →
Revan · ML Engineer · Darmstadt
σ 0.430
00%initializing
finding the structure in the noise
Revan