3 citations · 5 across the 13 of their papers we have counts for
6 papers · 1 filter
Enhancing Creativity in 3D Generative Design via a TRIZ-Inspired Text-to-CAD Framework
Dongeon Lee, Leekyo Jeong, Soyoung Yoo +2
Recent advances in large language models (LLMs) have demonstrated significant potential in supporting engineering design tasks, including computer-aided design (CAD) automation. Ho…
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…
Node Assigned physics-informed neural networks for thermal-hydraulic system simulation: CVH/FL module
Jeesuk Shin, Cheolwoong Kim, Sunwoong Yang +3
Severe accidents (SAs) in nuclear power plants have been analyzed using thermal-hydraulic (TH) system codes such as MELCOR and MAAP. These codes efficiently simulate the progressio…
AI-powered Digital Twin of the Ocean: Reliable Uncertainty Quantification for Real-time Wave Height Prediction with Deep Ensemble
Dongeon Lee, Sunwoong Yang, Jae-Won Oh +3
Environmental pollution and fossil fuel depletion have prompted the need for renewable energy-based power generation. However, its stability is often challenged by low energy densi…
Model-Agnostic AI Framework with Explicit Time Integration for Long-Term Fluid Dynamics Prediction
Sunwoong Yang, Ricardo Vinuesa, Namwoo Kang
This study addresses the critical challenge of error accumulation in spatio-temporal auto-regressive (AR) predictions within scientific machine learning models by exploring tempora…
Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction
Sunwoong Yang, Ricardo Vinuesa, Namwoo Kang
This study aims to overcome the limitations of conventional deep-learning approaches based on convolutional neural networks in complex geometries and unstructured meshes by explori…