A differentiable PCA-GPR surrogate framework for full-transient Bayesian inverse uncertainty quantification of black-box nuclear thermal-hydraulic systems

Abstract

Complex transient energy systems require uncertainty-quantified predictions, but Bayesian inverse uncertainty quantification (IUQ) is often impractical because posterior sampling with high-fidelity system codes requires many expensive full-transient simulations. This study develops a surrogate-assisted Bayesian IUQ framework that makes full-time-series calibration practical for the NACIE heavy-liquid-metal natural-circulation loop. The key innovation is to transform a black-box transient simulator into a differentiable probabilistic emulator by combining principal component analysis with Gaussian process regression (PCA--GPR). The surrogate reconstructs complete multichannel temperature and mass-flow histories from a compact latent representation, preserving dominant temporal and cross-channel correlations while enabling automatic differentiation of the log posterior. This allows both gradient-free ensemble slice sampling and gradient-informed sampling with the No-U-Turn Sampler and Microcanonical Langevin Monte Carlo. Five principal components capture more than 99\% of the output variance, and the surrogate achieves normalized root-mean-square errors below 2\%. Compared with direct Flownex SE evaluations, the surrogate reduces likelihood-evaluation cost by approximately 3000-fold while preserving posterior statistics and correlations. Posterior predictive analyses show reduced bias, narrower intervals, and improved agreement with measurements and independent validation.