Wing Kam Liu

发表

An enriched finite element method to fractional advection–diffusion equation

Shaoqiang Tang, Yanping Lian, G. Wagner, 2017, Computational Mechanics.

Data-driven discovery of dimensionless numbers and governing laws from scarce measurements

Zhengtao Gan, Xiaoyu Xie, A. Samaei, 2022, Nature Communications.

Hierarchical deep-learning neural networks: finite elements and beyond

Cheng Yu, Hengyang Li, Jiaying Gao, 2020, Computational Mechanics.

JAX-FEM: A differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science

Tianju Xue, Zhengtao Gan, Jian Cao, 2022, Comput. Phys. Commun..

Data-driven discovery of dimensionless numbers and governing laws from scarce measurements

Zhengtao Gan, Xiaoyu Xie, A. Samaei, 2021, Nature Communications.

Enriched reproducing kernel particle method for fractional advection–diffusion equation

Shaoqiang Tang, Yanping Lian, Yuping Ying, 2018, Acta Mechanica Sinica.

An introduction to kernel and operator learning methods for homogenization by self-consistent clustering analysis

Sourav Saha, Jiachen Guo, Wing Kam Liu, 2022, Computational Mechanics.

Enhancement of Endothelial Cell Retention on ePTFE Vascular Constructs by siRNA-Mediated SHP-1 or SHP-2 Gene Silencing

Adrian M. Kopacz, Shu Q. Liu, B. Tefft, 2015, Cellular and Molecular Bioengineering.

Deep Learning Discrete Calculus (DLDC): a family of discrete numerical methods by universal approximation for STEM education to frontier research

Sourav Saha, Chanwook Park, Jiachen Guo, 2022, Computational Mechanics.

Special issue of computational mechanics on machine learning theories, modeling, and applications to computational materials science, additive manufacturing, mechanics of materials, design and optimization

Shaofan Li, F. Chinesta, M. Bessa, 2023, Computational Mechanics.

Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration

G. Wagner, D. Apley, Tianju Xue, 2023, Computational Mechanics.

Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond

G. Wagner, D. Apley, Hengyang Li, 2023, Computational Mechanics.

Convolution Hierarchical Deep-Learning Neural Network Tensor Decomposition (C-HiDeNN-TD) for high-resolution topology optimization

D. Apley, Wei Chen, Hengyang Li, 2023, Computational Mechanics.

HiDeNN-FEM: a seamless machine learning approach to nonlinear finite element analysis

S. Mojumder, Chanwook Park, Ye Lu, 2023, Computational Mechanics.

Extended tensor decomposition model reduction methods: training, prediction, and design under uncertainty

S. Mojumder, Yang Li, Ye Lu, 2023, 2307.15873.

Isogeometric Convolution Hierarchical Deep-learning Neural Network: Isogeometric analysis with versatile adaptivity

Hengyang Li, Sourav Saha, S. Mojumder, 2023, Computer Methods in Applied Mechanics and Engineering.

Solving diffusive equations by proper generalized decomposition with preconditioner

Shaoqiang Tang, Wing Kam Liu, Xinyi Guan, 2023, Computational Mechanics.