ASME

Journal of Computing and Information Science in Engineering

Duhwan 
Mun, Ph.D.
Duhwan Mun

Areas of Interest

COMPUTER-AIDED DESIGN
KNOWLEDGE-BASED ENGINEERING
Korea University
Duhwan Mun is a Professor at the School of Mechanical Engineering at Korea University. He received a B.S. in Mechanical Engineering from Korea University, an M.S. and Ph.D. in Mechanical Engineering from KAIST. After graduation, he was a Senior Research Fellow at the Maritime & Ocean Engineering Research Institute (MOERI), a branch of Korea Ocean Research & Development Institute (KORDI), from 2006 to 2010. After that, he was an Assistant Professor/Associate Professor/Professor at the Department of Precision Mechanical Engineering at Kyungpook National University from 2010 to 2020. He serves as an Associate Editor for ASME Journal of Computing and Information Science in Engineering and the Journal of Computational Design and Engineering. His research interests include computer-aided design, industrial data standards for product data exchange, product lifecycle management, knowledge-based engineering, and virtual reality for engineering applications.

Return to the Editorial Board

More To Explore

Announcements

July 17 Spotlight: “Information Embedding in Additively Manufactured Parts Through Printing Speed Control” 

A recording is now available for the July 17, 2024 JCISE Spotlight talk by Professor Jitesh Panchal on paper co-authored with Karim A. ElSayed entitled “Information Embedding in Additively Manufactured Parts Through Printing Speed Control” J. Comput. Inf. Sci. Eng. J. Comput. Inf. Sci. Eng. Jul 2024, 24(7): 071005 (10 pages) Paper No: JCISE-23-1496 https://doi.org/10.1115/1.4065089.

Announcements

June 18, 2024 Spotlight: “Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset”

A recording is now available on Youtube for the June 18, 2024 Spotlight talk by Professor Christian Soize (Université Gustave Eiffel) on his paper “Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset,”

LATEST PAPERS

Latest Papers from ASME's Digital Collection