3 citations · 5 across the 5 of their papers we have counts for
5 papers
PhysicsBench: A Unified Leaderboard for Generative and Predictive Models in Engineering Design and Simulation
Sang Won Lee, Hyogu Jeong, Namwoo Kang
Generative and predictive artificial intelligence models are increasingly used to generate geometry and to predict physical fields and scalar quantities in engineering design and s…
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…
DeepJEB++: Foundation Model-Driven Large-Scale 3D Engineering Dataset via 2D Latent Space Augmentation
Soyoung Yoo, Leekyo Jeong, Jinsu Ra +4
Data-driven engineering design is constrained by the lack of large-scale 3D datasets that pair geometry with physics-based performance labels. In particular, existing 3D data augme…
Physics-Informed Machine Learning for Microscale Drying of Plant-Based Foods: A Systematic Review of Computational Models and Experimental Insights
C. P. Batuwatta-Gamage, H. Jeong, HCP Karunasena +3
This review examines the current state of research on microscale cellular changes during the drying of plant-based food materials (PBFM), with particular emphasis on computational…
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…