Proceedings of the
35th European Safety and Reliability Conference (ESREL2025) and
the 33rd Society for Risk Analysis Europe Conference (SRA-E 2025)
15 – 19 June 2025, Stavanger, Norway
Uncertainty-Aware Optimization in Engineered Systems via Gradient Boosting and Differential Evolution
1Civil and Systems Engineering, Johns Hopkins University, USA.
2Atmospheric Sciences Research Center, University at Albany, U.S.A.
3Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, U.S.A.
ABSTRACT
In engineered systems where safety and performance objectives are critical, accounting for various uncertainties-aleatoric and epistemic-is essential. Data sets offering useful information for decision-making can be expensive to collect and, hence, sparse. Distinction between all the underlying stochasticity through efficient data-driven approaches must be informed by selective strategies. In this work, we document our efforts in addressing a set of challenge problems posed by DNV and NASA personnel that require identifying the character and structure of the underlying stochasticity and then addressing reliability, performance, and optimization under uncertainty Agrell et al. (2025). We employ LightGBM for regression tasks, FLAML for hyperparameter tuning, and Differential Evolution for robust optimization using limited test data and simulation results on key response time series.
Keywords: Machine learning, Uncertainty quantification, Feature engineering, Optimization.
