Search Results for author: Yihang Gao

Found 6 papers, 1 papers with code

SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition

no code implementations16 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.

Transfer Learning

A Momentum Accelerated Adaptive Cubic Regularization Method for Nonconvex Optimization

no code implementations12 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.

regression

Approximate Secular Equations for the Cubic Regularization Subproblem

no code implementations27 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.

HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization

no code implementations23 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.

Image Generation

Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks

1 code implementation30 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.

Approximation for Probability Distributions by Wasserstein GAN

no code implementations18 Mar 2021 Yihang Gao, Michael K. Ng, Mingjie Zhou

According to our theoretical results, WGAN has higher requirement for the capacity of discriminators than that of generators, which is consistent with some existing theories.

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