PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations. - Jiahao Zhang 002; Shiqi Zhang 006; Guang Lin 001 (CoRR, 2022)

MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems. - Jiahao Zhang 002; Shiqi Zhang 006; Guang Lin 001 (CoRR, 2022)

RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification. - Jiahao Zhang 002; Shiqi Zhang 006; Guang Lin 001 (CoRR, 2022)

An Element-wise RSAV Algorithm for Unconstrained Optimization Problems. - Shiheng Zhang; Jiahao Zhang 002; Jie Shen 001; Guang Lin 001 (CoRR, 2023)

An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method. - Jiahao Zhang 002; Christian Moya; Guang Lin 001 (CoRR, 2024)

Energy-dissipative evolutionary deep operator neural networks. - Jiahao Zhang 002; Shiheng Zhang; Jie Shen 001; Guang Lin 001 (J. Comput. Phys., 2024)

A self-adaptive energy-based learning rate for stochastic gradient descent via Vector Auxiliary Variable method. - Jiahao Zhang 002; Christian Moya; Guang Lin 001 (Eng. Appl. Artif. Intell., 2025)

Energy-dissipative evolutionary Kolmogorov-Arnold networks for complex PDE systems. - Guang Lin 001; Changhong Mou; Jiahao Zhang 002 (J. Comput. Phys., 2025)

A Relaxed Vector Auxiliary Variable Algorithm for Unconstrained Optimization Problems. - Shiheng Zhang; Jiahao Zhang 002; Jie Shen 001; Guang Lin 001 (SIAM J. Sci. Comput., 2025)