Author

Shijie Zhong

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Preprint Sep 2026

A Bayesian Model Updating Framework for Systems Under Hybrid Uncertainties via Probability Integral Transform and Maximum Mean Discrepancy

Model updating under hybrid uncertainty is challenging because aleatory input variability makes the simulator output a probability distribution rather than a scalar, rendering the likelihood analytically intractable. Existing Approximate Bayesian Computation (ABC) methods typically employ nested Monte Carlo sampling, w...

Shi-Jie Zhong, Jiang-Feng Fu · 0 citations

Preprint Jul 2026

Analytical Extraction of Conditional Aleatory Sensitivities Across Epistemic Space via a Single PCE Model

In hybrid uncertainty quantification, evaluating how aleatory sensitivities vary under epistemic uncertainty, referred to as conditional Sobol'indices, is typically hindered by the computationally expensive double-loop procedure. Classical Polynomial Chaos Expansion (PCE) provides efficient access to global sensitivity...

Shijie Zhong, Huiyou Tan, Jiangfeng Fu · 0 citations

Preprint Jul 2026

Directional Kernel Mean Difference: A Fast Signed Statistic for Univariate Distribution Comparison

We introduce the Directional Kernel Mean Difference (DKMD), a signed statistic for univariate distribution comparison that preserves the direction of distributional shifts. Unlike the squared Maximum Mean Discrepancy (MMD), which discards directional information by squaring the RKHS distance, DKMD integrates the differ...

Shijie Zhong, Jiangfeng Fu · 0 citations