人工智能驱动的碰撞模拟代理的不确定性量化：基于开源保险杠梁基准的蒙特卡洛随机失活和深度集成的比较研究
Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark
AI总结：
研究人工智能驱动碰撞模拟代理的不确定性量化，对蒙特卡洛随机失活和深度集成两种方法在开源管道上比较，用具体随机失活解决常见批评，经汽车碰撞模拟实验，揭示权衡，表明可低成本实现良好校准和无超参数的不确定性估计。
AI中文摘要：
机器学习代理模型在工程产品开发中越来越多地被探索，以增强模拟驱动设计，提供近乎即时的预测，补充计算成本高昂的高保真分析。然而，一个关键差距限制了它们在安全关键工作流程中的应用：没有伴随不确定性估计的点预测无法告知工程师何时不应信任该模型。这项工作对两种广泛使用的不确定性量化方法——蒙特卡洛随机失活和深度集成——进行了系统的、直接的比较，应用于基于NVIDIA PhysicsNeMo构建的开源代理管道。一个关键贡献是使用具体随机失活，这是PhysicsNeMo的内置功能，通过在训练期间端到端学习来消除随机失活率作为手动超参数，直接解决了基于蒙特卡洛随机失活的不确定性量化最常见的批评。汽车碰撞模拟用作应用领域，以钢保险杠梁撞击问题作为基准。两种方法都在相同的保留模拟上进行评估，并在点精度、不确定性带校准和计算成本方面进行比较。结果揭示了准确性和校准之间的基本权衡，挑战了深度集成是代理不确定性量化默认黄金标准的常见假设。研究结果表明，在完全开源的工程工作流程中，可以以集成方法计算成本的一小部分实现校准良好、无超参数的不确定性估计。
英文摘要：
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.
