JOM Editorial Calendar - Topic Details

Augmenting Physics-based Models in ICME with Machine Learning & Uncertainty Quant.

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Guest Editor: Somnath Ghosh
Co-Guest Editors: David McDowell
Sponsored By: Other-Invited
Publication Date: January 2021
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.