Yiqi Gu(顾亦奇)'s Personal Website

School of Mathematical Sciences, University of Electronic Science and Technology of China

Professor

Qualified Ph.D. advisor

Email: yiqigu@uestc.edu.cn

My current CV ,

Research Gate , Google Scholar, Webpage at UESTC

Education

2008-2012  B.S. School of Mathematics,Zhejiang University

2012-2014  M.S. Department of Applied Mathematics,University of Washington

2014-2019  Ph.D. Department of Mathematics,Purdue University

Working Experiences

2019.08-2021.05  Research fellow,Department of Mathematics,National University of Singapore

2021.06-2023.02  Postdoctoal fellow,Department of Mathematics,The University of Hong Kong

2023.03-2023.10  Research fellow (tenure-track),School of Mathematical Sciences,University of Electronic Science and Technology of China

2023.11-present  Professor,School of Mathematical Sciences,University of Electronic Science and Technology of China

Courses and Seminars

Linear algebra and analytic geometry (undergraduate).
-  2023-2024 1st semester. Time: Tuesday 10:20-11:55; Thursday 14:30-16:05.

Mathematical foundations of deep learning.
-  2023-2024 2nd semester. Time: Wednesday/Friday 8:30-10:05.

Funding/Programs

2024  NSFC Excellent Young Scientists Fund (Overseas): Mechanism-based data-driven computation for equations of mathematical physics.

2024.1-2026.12  NSFC Major Research Plan: Analysis for the optimization convergence and generalization errors of physics-informed neural networks.

Research Projects

Spectral methods in complex domains;
Bound preserving numerical schemes for porous medium equations;
Deep learning for high-dimensional PDEs, discovery of dynamics, denoising, large linear systems ...

Publications

-  Y. Gao, Y. Gu and M. K. Ng, Gradient descent finds the global optima of physics-informed neural networks, Proceedings of the 40th International Conference on Machine Learning, 2023, 202, pp. 10676-10707.   pdf

-  Y. Gu and M. K. Ng, Deep neural networks for solving large linear systems arising from high-dimensional problems, SIAM Journal on Scientific Computing, 2023, 45(5), pp. A2356-A2381.   pdf

-  Y. Gu and M. K. Ng, Quadrature rule based discovery of dynamics by data-driven denoising, Journal of Computational Physics, 2023, 486, 112102.   pdf

-  Y. Gu and J. Shen, A fictitious domain spectral method for solving the Helmholtz equation in exterior domains, Journal of Scientific Computing, 2023, 94(3), 46.   pdf

-  Y. Gu, J. Harlim, S. Liang, and H. Yang, Stationary density estimation of itô diffusions using deep learning, SIAM Journal on Numerical Analysis, 2023, 61(1), pp. 45-82.   pdf

-  Y. Gu and M. K. Ng, Deep adaptive basis Galerkin method for high-dimensional evolution equations with oscillatory solutions, SIAM Journal on Scientific Computing, 2022, 44(5), pp. A3130-A3157.   pdf

-  Q. Du and Y. Gu, H. Yang and C. Zhou, The discovery of dynamics via linear multistep methods and deep learning: Error estimation, SIAM Journal on Numerical Analysis, 2022, 60(4), pp. 2014-2045.   pdf

-  Y. Gu and M. K. Ng, Deep Ritz method for the spectral fractional Laplacian equation using the Caffarelli-Silvestre extension, SIAM Journal on Scientific Computing, 2022, 44(4), pp. A2018-A2036.   pdf

-  Y. Gu, H. Yang and C. Zhou, SelectNet: Self-paced learning for high-dimensional partial differential equations, Journal of Computational Physics, 2021, 441, 110444.   pdf

-  Y. Gu, C. Wang and H. Yang, Structure probing neural network deflation, Journal of Computational Physics, 2021, 434, 110231.   pdf

-  Y. Gu and J. Shen, An efficient spectral method for elliptic PDEs in complex domains with circular embedding, SIAM Journal on Scientific Computing, 2021, 43(1), pp. A309-A329.   pdf

-  Y. Gu and J. Shen, Accurate and efficient spectral methods for elliptic PDEs in complex domains, Journal of Scientific Computing, 2020, 83(3), DOI: 10.1007/s10915-020-01226-9.   pdf

-  Y. Gu and J. Shen, Bound preserving and energy dissipative schemes for porous medium equations, Journal of Computational Physics, 2020, 410, 109378.   pdf

-  Y. Gu, X. Yang, M. Peng and G. Lin, Robust weighted SVD-type latent factor models for rating prediction, Expert Systems With Applications, 2019, 141, 112885.   pdf

-  Y. Gu and X. Cheng, A numerical approach for defect modes localization in an inhomogeneous medium, SIAM Journal on Applied Mathematics, 2013, 73(6), pp. 2188–2202.   pdf

 

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