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Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design

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Abstract

Significant advances in theory, simulation tools, advanced computing infrastructure, and experimental frameworks have enabled the field of materials science to become increasingly reliant on computer simulations. Theory-grounded computational models provide a better understanding of observed materials phenomena. At the same time, computational tools constitute an important ingredient of any framework that seeks to accelerate the materials development cycle. While simulations keep increasing in sophistication, formal frameworks for the quantification, propagation, and management of their uncertainties are required. Uncertainty analysis is fundamental to any effort to validate and verify simulations, which is often overlooked. Likewise, no simulation-driven materials design effort can be done with any level of robustness without properly accounting for the uncertainty in the predictions derived from the computational models. Here, we review some of the most recent works that have focused on the analysis, quantification, propagation, and management of uncertainty in computational materials science and ICME-based simulation-assisted materials design. Modern concepts of efficient uncertainty quantification and propagation, multi-scale/multi-level uncertainty analysis, model selection as well as model fusion are also discussed. While the topic remains relatively unexplored, there have been significant advances that herald an increased sophistication in the approaches followed for model validation and verification and model-based decision support.

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Acknowledgements

The authors would like to thank the support of the National Science Foundation (NSF) [Grant nos. CMMI-1534534, CMMI-1663130, DGE-1545403] as well as the Army Research Laboratory (ARL) through [Grant No. W911NF-132-0018]. Moreover, the authors would like to thank Prof. Alaa Elwany and Dr. Mohamad Mahmoudi for useful discussion on Bayesian networks applied to calibration of ICME model chains as well as for providing some of the figures used in this work. The authors also would like to thank Prof. Douglas Allaire and Mr. Meet Nilesh Sanghvi for useful discussions on the use of optimal importance weights approaches for the efficient propagation of uncertainty through model chains as well as for providing some of the figures reproduced here.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Texas A&M University, College Station, TX, USA

    Pejman Honarmandi & Raymundo Arróyave

  2. Department of Mechanical Engineering, Texas A&M University, College Station, TX, USA

    Raymundo Arróyave

  3. Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA

    Raymundo Arróyave

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  1. Pejman Honarmandi
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Correspondence to Pejman Honarmandi.

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Honarmandi, P., Arróyave, R. Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design. Integr Mater Manuf Innov 9, 103–143 (2020). https://doi.org/10.1007/s40192-020-00168-2

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