Machine Learning for Engineering Design

Jitesh H. Panchal, Mark Fuge, Ying Liu, Samy Missoum, and Conrad Tucker, Journal of Mechanical Design (2019).
Full text
DOI
Share

Abstract

Modern Machine Learning (ML) techniques are transforming many disciplines ranging from transportation to healthcare by uncovering pattern in data, developing autonomous systems that mimic human abilities, and supporting human decision-making. Modern ML techniques, such as deep neural networks, are fueling the rapid developments in artificial intelligence. Engineering design researchers have increasingly used and developed ML techniques to support a wide range of activities from preference modeling to uncertainty quantification in high-dimensional design optimization problems. This special issue brings together fundamental scientific contributions across these areas.

BibTeX Citation

@article{panchal2019machine,
title={Machine Learning for Engineering Design},
author={Panchal, Jitesh H and Fuge, Mark and Liu, Ying and Missoum, Samy and Tucker, Conrad},
journal={Journal of Mechanical Design},
pages={1--6},
year={2019},
publisher={American Society of Mechanical Engineers}
}