Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble
A new preprint presents a systematic comparison of Monte Carlo Dropout and Deep Ensembles for uncertainty quantification in AI-driven crash simulation surrogates, using an open-source bumper beam benchmark. The study leverages concrete dropout from NVIDIA PhysicsNeMo to eliminate manual hyperparameter tuning and evaluates both methods on accuracy, calibration, and computational cost. Results reveal a trade-off between accuracy and calibration, challenging the assumption that deep ensembles are always the gold standard, and show that well-calibrated, hyperparameter-free uncertainty estimates can be achieved at lower computational cost.
Why it matters: This work advances the reliability and efficiency of uncertainty quantification in safety-critical engineering simulations, potentially improving trust and adoption of AI surrogates in engineering workflows.
Full story at: arXiv Machine Learning ↗