Uncertainty quantification for AI-driven crash simulation surrogates: Monte Carlo dropout versus deep ensemble on an open-source bumper beam benchmark.
Read the original at arxiv.org→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...
Original headline: "Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark"