On the bumper-beam crash surrogate, learned-rate Monte Carlo Dropout achieved 100% empirical coverage at ±2σ, while a 10-member deep ensemble covered only 42% of timesteps.
Uncertainty Quantification in Machine Learning Using an Ensemble Approach with Gaussian Process Regression,
1 Pith paper cite this work, alongside 3 external citations. Polarity classification is still indexing.
1
Pith paper citing it
3
external citations · OpenAlex
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark
On the bumper-beam crash surrogate, learned-rate Monte Carlo Dropout achieved 100% empirical coverage at ±2σ, while a 10-member deep ensemble covered only 42% of timesteps.