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stat.ML2025
System-Level Uncertainty Quantification with Multiple Machine Learning Models: A Theoretical Framework
Xiaoping Du
ML models have errors when used for predictions. The errors are unknown but can be quantified by model uncertainty. When multiple ML models are trained using the same training poin…
stat.ML2025
Uncertainty Quantification for Machine Learning-Based Prediction: A Polynomial Chaos Expansion Approach for Joint Model and Input Uncertainty Propagation
Xiaoping Du
Machine learning (ML) surrogate models are increasingly used in engineering analysis and design to replace computationally expensive simulation models, significantly reducing compu…