Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions. - Yifei Zong; QiZhi He; Alexandre M. Tartakovsky (CoRR, 2022)

Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation. - Yifei Zong; David Barajas-Solano; Alexandre M. Tartakovsky (CoRR, 2024)

Randomized physics-informed machine learning for uncertainty quantification in high-dimensional inverse problems. - Yifei Zong; David Barajas-Solano; Alexandre M. Tartakovsky (J. Comput. Phys., 2024)

Physics-informed machine learning method with space-time Karhunen-Loève expansions for forward and inverse partial differential equations. - Alexandre M. Tartakovsky; Yifei Zong (J. Comput. Phys., 2024)

Mathematics of Digital Twins and Transfer Learning for PDE Models. - Yifei Zong; Alexandre M. Tartakovsky (CoRR, 2025)

VAE-DNN: Energy-Efficient Trainable-by-Parts Surrogate Model For Parametric Partial Differential Equations. - Yifei Zong; Alexandre M. Tartakovsky (CoRR, 2025)