12 citations · 28 across the 18 of their papers we have counts for
18 papers
Risk-Aware Generative Inpainting for Optimized Design Editing of EV Battery Cooling Channels
Leekyo Jeong, Yoon Koo Lee, Namwoo Kang
Cooling-channel layouts for electric-vehicle battery packs must deliver temperature uniformity and low pressure drop while maintaining a single continuous channel. In late-stage de…
Distance-Aware Attention and Wall-Distance Expert Routing for Transformer-Based 3D Flow Prediction
Sanghyeon Kim, Sunwoong Yang, Namwoo Kang
Transformer surrogates for 3D flow prediction compress an industrial mesh into a small set of tokens from which every prediction point reads. Two operations follow: the retrieval s…
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…
A Multi-Agent System for Motor Design Optimization via an FEA-AI Hybrid Approach
Jinseong Han, Sunwoong Yang, Namwoo Kang
This study presents a large language model (LLM)-based multi-agent framework for interior permanent magnet synchronous motor (IPMSM) design optimization that mitigates limitations…
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…
Point-wise Diffusion Models for Physical Systems with Shape Variations: Application to Spatio-temporal and Large-scale system
Jiyong Kim, Sunwoong Yang, Namwoo Kang
This study introduces a novel point-wise diffusion model that processes spatio-temporal points independently to efficiently predict complex physical systems with shape variations.…
PyTopo3D: A Python Framework for 3D SIMP-based Topology Optimization
Jihoon Kim, Namwoo Kang
Three-dimensional topology optimization (TO) is a powerful technique in engineering design, but readily usable, open-source implementations remain limited within the popular Python…
Modular Mechanism Design Optimization in Large-Scale Systems with Manufacturing Cost Considerations
Sumin Lee, Namwoo Kang
Modular design maximizes utility by using standardized components in large-scale systems. From a manufacturing perspective, it supports green technology by reducing material waste…
Data-Efficient Deep Operator Network for Unsteady Flow: A Multi-Fidelity Approach with Physics-Guided Subsampling
Sunwoong Yang, Youngkyu Lee, Namwoo Kang
This study presents an enhanced multi-fidelity Deep Operator Network (DeepONet) framework for efficient spatio-temporal flow field prediction when high-fidelity data is scarce. Key…
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…
Vehicle Suspension Recommendation System: Multi-Fidelity Neural Network-based Mechanism Design Optimization
Sumin Lee, Namwoo Kang
Mechanisms are designed to perform functions in various fields. Often, there is no unique mechanism that performs a well-defined function. For example, vehicle suspensions are desi…
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…
Deep Generative Design for Mass Production
Jihoon Kim, Yongmin Kwon, Namwoo Kang
Generative Design (GD) has evolved as a transformative design approach, employing advanced algorithms and AI to create diverse and innovative solutions beyond traditional constrain…
Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditions
Sumin Lee, Jihoon Kim, Namwoo Kang
Mechanisms are essential components designed to perform specific tasks in various mechanical systems. However, designing a mechanism that satisfies certain kinematic or quasi-stati…
Multi-objective Generative Design Framework and Realization for Quasi-serial Manipulator: Considering Kinematic and Dynamic Performance
Sumin Lee, Sunwoong Yang, Namwoo Kang
This paper proposes a framework that optimizes the linkage mechanism of the quasi-serial manipulator for target tasks. This process is explained through a case study of 2-degree-of…
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
Sunwoong Yang, Hojin Kim, Yoonpyo Hong +3
This study explores the potential of physics-informed neural networks (PINNs) for the realization of digital twins (DT) from various perspectives. First, various adaptive sampling…