CRADIPOR: Crash Dispersion Predictor

arXiv cs.LG / 5/4/2026

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Key Points

The paper introduces CRADIPOR, a numerical dispersion prediction tool intended to improve reliability of post-processing results in automotive crash finite-element simulations.
It addresses the problem that FE-based crash predictions are not strictly repeatable due to parallel computation and model complexity, making engineering performance criteria harder to trust.
CRADIPOR uses a Rank Reduction Autoencoder (RRAE) together with supervised classification to identify regions most sensitive to numerical dispersion without rerunning costly simulations.
The study reports that the RRAE-based approach outperforms a Random Forest baseline on the authors’ dataset.
Among evaluated signal representations, slope-based (including slope variations) and wavelet-based inputs are the most promising, with slope variations delivering the best classification performance.

Abstract

We present CRADIPOR, a numerical dispersion prediction tool for automotive crash simulations. Finite Element (FE) crash models are widely used throughout vehicle development, but their predictions are not strictly repeatable because of parallel computation and model complexity. As a result, performance criteria evaluated during post-processing may exhibit significant numerical dispersion, which complicates engineering decision-making. Although dispersion can be estimated by repeating the same simulation, this approach is generally impractical because of its high computational cost. This work therefore investigates a prediction tool that can be applied during routine crash-simulation post-processing without repeating the computation. The proposed approach relies on a Rank Reduction Autoencoder (RRAE) combined with supervised classification in order to identify regions sensitive to numerical dispersion. The comparative analysis suggests that the RRAE-based framework is more effective than the Random Forest baseline on the studied dataset. Among the tested signal representations, wavelet-based and slope-based inputs appear to be the most promising, with slope variations providing the best classification performance. These results support the use of structured latent representations for improving numerical-dispersion detection in automotive crash post-processing.