Uncertainty-Aware Optimization in Engineered Systems via Gradient Boosting and Differential Evolution

In engineered systems where safety and performance objectives are critical, accounting for various uncertaintiesaleatoric 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.