Publications

Google Scholar

Journal Papers (Mathematics)

• 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)

Conference Papers (Computer Science/ML/AI)

• Chuanyang Zheng*, Yihang Gao*, Han Shi, Minbin Huang, Jingyao Li, Jing Xiong, Xiaozhe Ren, Michael K. Ng, Xin Jiang, Zhenguo Li, Yu Li. DAPE: Data-Adaptive Positional Encoding for Length Extrapolation, Advances in Neural Information Processing Systems 2024 (NeurIPS 2024). (* indicates equal contribution) arxiv

• Yihang Gao, Yiqi Gu, 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, 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, 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)