- Enrico Crovini, Simon L. Cotter, Konstantinos Zygalakis, Andrew B. Duncan. Batch Bayesian Optimization via Particle Gradient Flows. SIAM/ASA J. Uncertain. Quantification, 14(1):197-220, 2026.
- Grigorios A. Pavliotis, Renato Spacek, Gabriel Stoltz, Urbain Vaes. Neural Network Approaches for Variance Reduction in Fluctuation Formulas. SIAM/ASA J. Uncertain. Quantification, 14(1):221-255, 2026.
- Paz Fink Shustin, Shashanka Ubaru, Malgorzata J. Zimon, Songtao Lu, Vasileios Kalantzis, Lior Horesh, Haim Avron. PCE-Net : High-Dimensional Surrogate Modeling for Learning Uncertainty. SIAM/ASA J. Uncertain. Quantification, 14(1):168-196, 2026.
- Shijie Zhong, Wanggang Shen, Tommie Catanach, Xun Huan. Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models Using Markov Chain Monte Carlo. SIAM/ASA J. Uncertain. Quantification, 14(1):19-47, 2026.
- Tim Gyger, Reinhard Furrer, Fabio Sigrist. Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data. SIAM/ASA J. Uncertain. Quantification, 14(1):142-167, 2026.
- Daniela Calvetti, Erkki Somersalo. Subspace Splitting Fast Sampling from Gaussian Posterior Distributions of Linear Inverse Problems. SIAM/ASA J. Uncertain. Quantification, 14(1):111-141, 2026.
- Fernando HenrĂquez, Ignacio Labarca-Figueroa. Domain Uncertainty Quantification for the Lippmann-Schwinger Volume Integral Equation. SIAM/ASA J. Uncertain. Quantification, 14(1):77-110, 2026.
- Ying Wu, Shifeng Xiong, Peter Chien. Random Fourier Features Based Gaussian Process Models for Stochastic Simulations. SIAM/ASA J. Uncertain. Quantification, 14(1):48-76, 2026.
- Xinming Wang, Simon Mak, John Miller, Jianguo Wu. Local Transfer Learning Gaussian Process Modeling, with Applications to Surrogate Modeling of Expensive Computer Simulators. SIAM/ASA J. Uncertain. Quantification, 14(1):256-286, 2026.
- Shangkun Wang, V. Roshan Joseph. Active Learning via Heteroskedastic Rational Kriging. SIAM/ASA J. Uncertain. Quantification, 14(1):1-18, 2026.