JOM Editorial Calendar - Topic Details

Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification

Manuscript Submission Deadline: July 01, 2020
Guest Editor: Somnath Ghosh
Co-Guest Editors: David McDowell
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Sponsored By:
Publication Date: December 2020
Keywords: Computational Materials Science & Engineering, Machine Learning, Uncertainty Quantification
Scope: This topic will include papers on modeling complex material behavior and failure characteristics at multiple scales, using ICME and physics-based simulation tools augmented by machine learning and uncertainty quantification. Machine learning, using datasets from experiments and validated simulation tools, can unravel novel material models and physical phenomena. It is necessary to couple these predictions with uncertainty quantification to understand levels of error and ways to mitigate uncertainty.
Call for Papers: Download

How to Submit a Manuscript

Please read the detailed Instructions for Authors and upload your manuscript at the Editorial Manager website for JOM. To ensure sufficient time for peer review, papers will not be accepted after the posted manuscript submission deadline. Original research papers should be 3,000-9,000 words with up to 12 figures maximum; review papers should be 6,000-11,000 words with up to 20 figures maximum.

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