1 code implementation • 30 Aug 2021 • Yihang Gao, Michael K. Ng
In this paper, we study a physics-informed algorithm for Wasserstein Generative Adversarial Networks (WGANs) for uncertainty quantification in solutions of partial differential equations.
no code implementations • 18 Mar 2021 • Yihang Gao, Michael K. Ng, Mingjie Zhou
Studied here are Wasserstein generative adversarial networks (WGANs) with GroupSort neural networks as their discriminators.
no code implementations • 23 May 2022 • Yihang Gao, Huafeng Liu, Michael K. Ng, Mingjie Zhou
Wide applications of differentiable two-player sequential games (e. g., image generation by GANs) have raised much interest and attention of researchers to study efficient and fast algorithms.
no code implementations • 27 Sep 2022 • Yihang Gao, Man-Chung Yue, Michael K. Ng
In this paper, we propose and analyze a novel CRS solver based on an approximate secular equation, which requires only some of the Hessian eigenvalues and is therefore much more efficient.
no code implementations • 12 Oct 2022 • Yihang Gao, Michael K. Ng
The cubic regularization method (CR) and its adaptive version (ARC) are popular Newton-type methods in solving unconstrained non-convex optimization problems, due to its global convergence to local minima under mild conditions.
no code implementations • 16 Nov 2022 • Yihang Gao, Ka Chun Cheung, Michael K. Ng
Physics-informed neural networks (PINNs) have attracted significant attention for solving partial differential equations (PDEs) in recent years because they alleviate the curse of dimensionality that appears in traditional methods.
no code implementations • 21 Feb 2024 • Yihang Gao, Chuanyang Zheng, Enze Xie, Han Shi, Tianyang Hu, Yu Li, Michael K. Ng, Zhenguo Li, Zhaoqiang Liu
Previous works try to explain this from the expressive power and capability perspectives that standard transformers are capable of performing some algorithms.