Yihang Gao, Michael K. Ng, Mingjie Zhou. Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks, SIAM Journal on Mathematics of Data Science, 2023. paper
Yihang Gao, Xuelei Lin, Michael K. Ng. Blind Deconvolution for Multiple Observed Images with Missing Values, Pacific Journal of Optimization, 2023. paper
Yihang Gao and Michael K. Ng. Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks, Journal of Computational Physics, 2022. paper (arxiv)
Yihang Gao, Yiqi Gu and Michael K. Ng. Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks, 40th International Conference on Machine Learning 2023 (ICML 2023). paper
Yihang Gao, Man-Chung Yue and Michael K. Ng. Approximate Secular Equations for the Cubic Regularization Subproblem, Advances in Neural Information Processing Systems 2022 (NeurIPS 2022). paper / poster / slides
Yihang Gao, Ka Chun Cheung and Michael K. Ng. SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition, IEEE Symposium Series on Computational Intelligence 2022 (IEEE SSCI 2022). paper (arxiv)