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

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. However, a critical gap limits their adoption in safety-critical workflows: a point prediction without an accompanying uncertainty estimate cannot tell an engineer when the model should not be trusted. This work presents a systematic, head-to-head comparison of two widely used uncertainty quantification approaches -- Monte Carlo Dropout and Deep Ensembles -- applied to an open-source surrogate pipeline built on NVIDIA PhysicsNeMo. A key contribution is the use of concrete dropout, a built-in PhysicsNeMo capability that eliminates the dropout rate as a manual hyperparameter by learning it end-to-end during training, directly addressing the most common criticism of Monte Carlo Dropout-based uncertainty quantification. Automotive crash simulation is used as the application domain, with a steel bumper beam impact problem serving as the benchmark. Both methods are evaluated on identical held-out simulations and compared on point accuracy, uncertainty band calibration, and computational cost. The results reveal a fundamental trade-off between accuracy and calibration that challenges the common assumption that deep ensembles are the default gold standard for surrogate uncertainty quantification. The findings demonstrate that well-calibrated, hyperparameter-free uncertainty estimates are achievable within a fully open-source engineering workflow at a fraction of the computational cost of ensemble approaches.