Efficient Surrogate Modeling with Dimensionality Reduction for Robust Design Optimization

The authors propose a reduced dimension variational Gaussian process (RDVGP) surrogate model that efficiently approximates complex computational models with high-dimensional and uncertain inputs. The RDVGP surrogate incorporates dimensionality reduction and Bayesian inference to capture both epistemic and aleatoric uncertainties.