# CRADIPOR: Crash Dispersion Predictor

arXiv: https://arxiv.org/abs/2605.00070 · HTML: https://mlsanity.com/paper/2605.00070/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Rank Reduction Autoencoders](https://mlsanity.com/paper/2405.13980/) (2024)
- [RRAEDy: Adaptive Latent Linearization of Nonlinear Dynamical Systems](https://mlsanity.com/paper/2512.07542/) (2025)
- [Generative Parametric Design (GPD): A framework for real-time geometry generation and on-the-fly multiparametric approximation](https://mlsanity.com/paper/2512.11748/) (2025)
- [Data-driven discovery of roughness descriptors for surface characterization and intimate contact modeling of unidirectional composite tapes](https://mlsanity.com/paper/2603.20418/) (2026)

## Abstract (arXiv)

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.