arXiv Machine Learning July 22, 2026 By Sudeep Chavare

Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark

Read the original on arXiv Machine Learning →

arXiv:2607. 18294v1 Announce Type: new Abstract: Machine learning surrogate models are increasingly being explored in engineering product development to augment simulation-driven design, offering near-instantaneous predictions that complement computationally expensive high-fidelity analyses.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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