Machine Learning for Engineering Design

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

  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},
  publisher={American Society of Mechanical Engineers}