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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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.00070 [cs.LG] |
| (or arXiv:2605.00070v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.00070 arXiv-issued DOI via DataCite |
From: Sebastian Rodriguez [view email]
[v1]
Thu, 30 Apr 2026 11:10:16 UTC (569 KB)
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