Namwoo Kang 

연구분야 > 지능형 모빌리티 기술 (en)연구분야 > 친환경 모빌리티 기술전임교수 (en)






Research Interest

Mobility Design, Generative Design, Data-driven Design, Machine Learning, Deep Learning, Design Optimization, Topology Optimization, CAD/CAM/CAE, HCI


  • PhD – Design Science, University of Michigan
  • MS – Technology and Management, Seoul National University
  • BA – Mechanical and Aerospace Engineering, Seoul National University


Namwoo Kang is an associate professor of Cho Chun Shik Graduate School of Mobility at KAIST. He is also currently CEO of Narnia Labs. Before joining KAIST, he was an assistant professor of Department of Mechanical Systems Engineering at Sookmyung Women’s University and a research fellow in the Department of Mechanical Engineering at the University of Michigan. In addition, he worked at Hyundai Motor Company as a Research Engineer.
He has earned his Ph.D. in Design Science (concentration in Mechanical Engineering and Marketing) at the University of Michigan. Previously, he obtained a M.S. degree in Technology and Management and B.S. in Mechanical and Aerospace Engineering from Seoul National University.
He has been pursuing AI-powered Generative Design research by integrating physics and data for virtual mobility/product development. His research interests include mobility design, generative design, data-driven design, machine learning, deep learning, design optimization, topology optimization, CAD/CAM/CAE, and HCI


  • Kim, E., Ryu, H., Oh, H., and Kang, N.* (2022) “Safety Monitoring System of Personal Mobility Driving Using Deep Learning”, Journal of Computational Design and Engineering (Accepted)
  • Jang, S., Yoo, S., and Kang, N.* (2022) “Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs”, Computer-Aided Design, 146, 103225
  • Kim, S., Lee, U., Lee, I., and Kang, N.* (2022) “Idle Vehicle Relocation Strategy through Deep Learning for Shared Autonomous Electric Vehicle System Optimization”, Journal of Cleaner Production, 333, 130055
  • Lee, S., Yoo, S., Kim, S., Kim, E, and Kang, N.* (2021) “Effect of Robo-taxi User Experience on User Acceptance: Field Test Data Analysis”, Transportation Research Record: Journal of the Transportation Research Board, 2676(2), pp. 350–366
  • Yoo, S. and Kang, N.* (2021) “Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization”, Expert Systems with Applications, 183, 115430
  • Yoo, S., Lee, S., Kim, S., Hwang, K. H, Park, J. H., and Kang, N.* (2021) “Integrating Deep Learning into CAD/CAE System: Generative Design and Evaluation of 3D Conceptual Wheel”, Structural and Multidisciplinary Optimization, 64(4), pp. 2725-2747
  • Lee, K. J., Kang, N.*, Kokkolaras, M., and Papalambros, P. Y. (2021) “Design Optimization of a Hybrid Electric Vehicle Cooling System Considering Performance and Packaging”, International Journal of Vehicle Design, 85(2/3/4), pp. 154-177
  • Lee, U., Kang, N.*, and Lee I. (2020) “Choice Data Generation using Usage Scenarios and Discounted Cash Flow Analysis”, Journal of Choice Modelling, 37, 100250
  • Jung, Y., Lee, J., Lee, M., Kang, N.*, and Lee I. (2020) “Probabilistic Analytical Target Cascading using Kernel Density Estimation for Accurate Uncertainty Propagation”, Structural and Multidisciplinary Optimization, 61(5), pp. 2077-2095
  • Kim, S., Chang, J., Park, H. H., Song, S. U., Cha, C. B., Kim, J. W., Kang, N.* (2020) “Autonomous Taxi Service Design and User Experience”, International Journal of Human–Computer Interaction, 36(5), pp. 429-448
  • Lee, U., Kang, N.*, and Lee I. (2020) “Shared Autonomous Electric Vehicle Design and Operations Under Uncertainties: A Reliability-based Design Optimization Approach”, Structural and Multidisciplinary Optimization, 61, pp. 1529–1545
  • Oh, S., Jung, Y., Kim, S., Lee, I., and Kang, N.* (2019) “Deep Generative Design: Integration of Topology Optimization and Generative Models”, Journal of Mechanical Design, 141(11), 111405
  • Kang, N., Feinberg, F. M., and Papalambros, P. Y. (2019) “Designing Profitable Joint Product–Service Channels: Case Study on Tablet and eBook Markets”, Design Science, Vol. 5, e12
  • Koh, S. R., Hur, S. H., and Kang, N.* (2019) “Feasibility Study on the Korean Government’s Hybrid Conversion Project of Small Diesel Trucks for Parcel Delivery Services”, Journal of Cleaner Production, 232, pp.559-574
  • Lee, U., Kang, N.*, and Lee, I. (2019) “Selection of optimal target reliability in RBDO through reliability-based design for market systems (RBDMS) and application to electric vehicle design”, Structural and Multidisciplinary Optimization, 60(3), pp.949–963
  • Kang, N., Bayrak, A., and Papalambros, P. Y. (2018) “Robustness and Real Options for Vehicle Design and Investment Decisions under Gas Price and Regulatory Uncertainties”, Journal of Mechanical Design, 140(10), 101404
  • Jung, Y., Kang, N., and Lee I. (2018) “Modified Augmented Lagrangian Coordination and Alternating Direction Method of Multipliers with Parallelization in Non-hierarchical Analytical Target Cascading”, Structural and Multidisciplinary Optimization, 58(2), pp. 555-573
  • Kang, N., Burnap, A., Kim, K. H., Reed, M. P., and Papalambros, P. Y. (2017) “Influence of Automobile Seat Form and Comfort Rating on Willingness to Pay”, International Journal of Vehicle Design, 75(1/2/3/4), pp.75-90
  • Kang, N., Feinberg, F. M., and Papalambros, P. Y. (2017) “Autonomous Electric Vehicle Sharing System Design”, Journal of Mechanical Design, 139(1), 011402
  • Bayrak, A., Kang, N.*, and Papalambros, P. Y. (2016) “Decomposition Based Design Optimization of Hybrid Electric Powertrain Architectures: Simultaneous Configuration and Sizing Design”, Journal of Mechanical Design, 138(7), 071405
  • Kang, N., Ren, Y., Feinberg, F. M., and Papalambros, P. Y. (2016) “Public Investment and Electric Vehicle Design: A Model-based Market Analysis Framework with Application to a USA-China Comparison Study”, Design Science, Vol. 2, e6
  • D’Souza, K., Bayrak, A. E., Kang, N., Wang, H., Altin, B., Barton, K., Hu, J., Papalambros, P. Y., Epureanu, B. I., and Gerth, R. (2016) “An Integrated Design Approach for Evaluating the Effectiveness and Cost of a Fleet”, Journal of Defense Modeling and Simulation, 13(4), pp. 381-397
  • Kang, N., Feinberg, F. M., and Papalambros, P. Y. (2015) “Integrated Decision Making in Electric Vehicle and Charging Station Location Network Design”, Journal of Mechanical Design, 137(6), 061402
  • Kang, N., Kokkolaras, M., Papalambros, P. Y., Park, J., Na, W., Yoo, S., and Featherman, D. (2014) “Optimal Design of Commercial Vehicle Systems Using Analytical Target Cascading”, Structural and Multidisciplinary Optimization, 50(6), pp. 1103-1114
  • Kang, N., Kokkolaras, M., and Papalambros, P. Y. (2014) “Solving Multiobjective Optimization Problem Using Quasi-separable MDO Formulations and Analytical Target Cascading”, Structural and Multidisciplinary Optimization, 50(5), pp. 849-859
  • Kang, N., Kim, J. and Park, Y. (2007) “Integration of marketing domain and R&D domain in NPD design process”, Industrial Management & Data Systems, 107(6), pp. 780-801


– Young Researcher Award, CAE and Applied Mechanics, Korean Society of Mechanical Engineers (KSME), 2022

– Young Scientist Award, Asian Society for Structural and Multidisciplinary Optimization (ASSMO), 2020

– Future Technology Award, Computational Structural Engineering Institute of Korea (COSEIK), 2019

– Altair Fellow, Altair, 2015

– Dow Distinguished Award, Dow Sustainability Fellows, University of Michigan, 2014


인공지능 기반 제너레이티브 디자인 (DEEP GENERATIVE DESIGN)

인공지능이 공학시스템을 스스로 설계할 수 있다면 어떨까요? 스마트설계연구실(Smart Design Lab)은 인공지능 기반의 공학설계로 제조업의 새로운 패러다임을 열고자 합니다. 물리 기반의 역학과 데이터 기반의 딥러닝 기술을 결합하고, 디지털 트랜스포메이션을 통한 가상 제품 개발 플랫폼을 구축합니다. 이를 통해 제품개발에 소요되는 시간과 비용을 혁신적으로 절감시키고, 시장에서 성공할 수 있는 설계를 가능하게 합니다.

인공지능과 공학설계기술(최적설계, CAD/CAM/CAE, HCI 등)을 융합한 “인공지능 기반 제너레이티브 디자인”을 통해 인공지능이 스스로 공학성능/심미성/경제성을 만족시키는 설계를 해내도록 합니다. 아래 4단계에 필요한 요소기술들을 개발하고 통합하여 사용합니다.

① 설계생성: 과거 설계 데이터를 기반으로 심미적이고 공학적으로 타당한 설계안을 대량 생성하는 기술
– Generative Design + AI
– Topology Optimization + AI
– Parametric Design + AI
– CAD Automation + AI

② 설계평가: 생성된 설계안의 다양한 공학성능, 제조성, 독창성 등을 실시간으로 예측하고 원인을 설명하는 기술
– Engineering Performance Prediction + AI
– Manufacturing Cost Prediction + AI
– Manufacturability Prediction + AI
– Design Novelty Evaluation + AI
– Design Clustering + AI
– Explainable Design Visualization + AI

③ 설계최적화: 목표성능과 제약조건을 만족시키는 최적 설계안을 실시간으로 도출하는 기술
– Inverse Design + AI
– Design Optimization + AI
– Multidisciplinary Design Optimization + AI

④ 설계추천: 설계안에 대한 고객 선호도를 실시간으로 예측하고 시장성 높은 설계를 추천하는 기술
– Design Aesthetics Evaluation + AI
– Customer Preference & Market Share Prediction + AI
– Design Recommendation + AI
– Market Segmentation + AI
– Price Decision Making + AI



  • – 인공지능 기반 모빌리티 설계: Deep Generative Design (PDF)
  • – 설계 프로세스 혁신을 위한 AI 기반의 접근 방법 (PDF)
  • – 대한기계학회 강습회: Data-driven Design (PDF)
  • – 전산구조공학 Special Article: 인공지능과 최적설계 (PDF)
  • – 기계저널 특별기획: CAE 및 응용역학에서의 인공지능/머신러닝 활용 (PDF)


스마트설계연구실(Smart Design Lab)에서 진행중인 세부 연구 주제들을 소개합니다.
모빌리티, 전자제품, 플랜트 등 다양한 제조분야 설계 Application을 다룹니다.
아래 그림을 클릭하시면 대표연구들을 확인하실 수 있습니다.



함께 연구한 기관과 기업을 소개합니다. 다양한 분야의 협업을 기다립니다.


  • GT833 AI-based Mobility Design (Graduate course) 
  • CoE491 Smart Mobility Design for Designer, Engineer, and Data Scientist (Undergraduate course)
  • ME340 Engineering Design (Undergraduate course)