Introduction

ZHANG, Lian

Biography

POSITION/TITLE

Research Scientist

RESEARCH FIELD

Scientific and engineering computing, fluid-structure interaction, deep learning, computer vision

EMAIL

zhanglian@sribd.cn

EDUCATION BACKGROUND

BSc at Zhejiang University 2011-2015

Ph.D at Hong Kong University of Science and Technology 2015-2019

BIOGRAPHY

Lian Zhang graduated from Zhejiang University in 2015 and received his Ph.D degree at Hong Kong University of Science and Technology in 2019. From 2019 to 2020, he worked as a postdoc at Penn State University, and then worked at In-Chao Institute Ltd. He joined SRIBD in December 2022. His research interest covers scientific and engineering computing and deep learning, especially the fluid-structure interaction problem and image processing.

ACADEMIC PUBLICATIONS

  • Juncai He, Jinchao Xu, Lian Zhang, Jianqing Zhu. An Interpretive Constrained Linear Model for ResNet and MgNet, Neural Networks, 162, 384-392 2023.
  • Xiaofeng Xu, Lian Zhang, Yin Shi, Long-Qing Chen, Jinchao Xu. Integral Boundary Conditions in Phase Field Models, Computer and Mathematics with Application, 135, 1–5, 2023.
  • Mingchao Cai, Mo Mu, Lian Zhang. Decoupling Techniques for Coupled PDE Models in Fluid Dynamics, Book chapter in The Essence of Large-Eddy Simulations, 10.5772/intechopen.105997, 2022.
  • Jianhong Chen, Wenrui Hao, Pengtao Sun and Lian Zhang. Predict Blood Pressure by Photoplethysmogram with the Fluid-Structure Interaction Modeling, Communications in Computational Physics 31(4), 1114-1133, 2022.
  • Wenrui Hao, Pengtao Sun, Jinchao Xu and Lian Zhang. An efficient computational approach for solving the fluid-structure interaction problem with an application in aneurysm, Journal of Computational Physics 433, 110181, 2021.
  • Juncai He, Xiaodong Jia, Jinchao Xu, Lian Zhang, and Liang Zhao. Make l1 Regularization Effective in Training Sparse CNN, Computational Optimization and Applications 77, 163-182, 2020.
  • Lian Zhang, Mingchao Cai and Mo Mu. A multirate approach for fluid-structure interaction computation with decoupled methods, Communications in Computational Physics 27, 1014-1031, 2020.