92 citations · 103 across the 7 of their papers we have counts for
7 papers
Trajectory-Aware Flow Matching for Topology Optimisation
Shusheng Xiao, Jinshuai Bai, Hyogu Jeong +3
Topology optimisation (TO) often requires repeated finite element analysis and sensitivity-based material updates, which can be costly when multiple candidate designs are needed un…
Steering topology distributions for unified generative design of architected metamaterials
Haolin Li, Yuyang Miao, Menglei Li +9
Architected metamaterials derive their functions from structure, creating vast opportunities to program physical responses through topology design. However, existing design methods…
Discrete Solution Operator Learning for Geometry-Dependent PDEs
Jinshuai Bai, Haolin Li, Zahra Sharif Khodaei +3
Neural operator learning accelerates PDE solution by approximating operators as mappings between continuous function spaces. Yet in many engineering settings, varying geometry indu…
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…
Physics-informed neural network for friction-involved nonsmooth dynamics problems
Zilin Li, Jinshuai Bai, Huajing Ouyang +8
Friction-induced vibration (FIV) is very common in engineering areas. Analysing the dynamic behaviour of systems containing a multiple-contact point frictional interface is an impo…
Utilising physics-guided deep learning to overcome data scarcity
Jinshuai Bai, Laith Alzubaidi, Qingxia Wang +3
Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challen…
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…