Publications (6)
An introduction to programming Physics-Informed Neural Network-based computational solid mechanics
Jinshuai Bai, Hyogu Jeong, C. P. Batuwatta-Gamage +6
Physics-informed neural network (PINN) has recently gained increasing interest in computational mechanics. In this work, we present a detailed introduction to programming PINN-base…
Molecular dynamics simulation of crack growth in mono-crystal nickel with voids and inclusions
Zhenxing Cheng, Hu Wang, Gui-Rong Liu +1
In this study, the crack propagation of the pre-cracked mono-crystal nickel with the voids and inclusions has been investigated by molecular dynamics simulations. Different sizes o…
Fatigue crack propagation in carbon steel using RVE based model
Zhenxing Cheng, Hu Wang, Gui-Rong Liu
A representative volume element (RVE) based multi-scale method is proposed to investigate the mechanism of fatigue crack propagation by the molecular dynamics (MD) and the extended…
Improved neighbor list algorithm in molecular simulations using cell decomposition and data sorting method
Zhenhua Yao, Jian-Sheng Wang, Gui-Rong Liu +1
An improved neighbor list algorithm is proposed to reduce unnecessary interatomic distance calculations in molecular simulations. It combines the advantages of Verlet table and cel…
Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEs
Jinshuai Bai, Gui-Rong Liu, Ashish Gupta +3
Our recent intensive study has found that physics-informed neural networks (PINN) tend to be local approximators after training. This observation leads to this novel physics-inform…
Thermal conduction of carbon nanotubes using molecular dynamics
Zhenhua Yao, Jian-Sheng Wang, Baowen Li +1
The heat flux autocorrelation functions of carbon nanotubes (CNTs) with different radius and lengths is calculated using equilibrium molecular dynamics. The thermal conductance of…