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Uncertainty quantification of metallic microstructures using principal image moments · Study

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ArticleUncertainty quantification of metallic microstructures using principal image moments

Arulmurugan Senthilnathan2022

Article2022

Uncertainty quantification of metallic microstructures using principal image moments

Arulmurugan Senthilnathan · Iman Javaheri · Hengduo Zhao · Veera Sundararaghavan · Marc DeGraef · Pınar Acar

8 citations·0/0 files stored·Computational Materials Science

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Probabilistic and Robust Engineering Design Machine Learning in Materials Science Composite Material Mechanics

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References

1990The origins of kriging 2005Use of Kriging Models to Approximate Deterministic Computer Models 2020Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design 2016Uncertainty quantification of microstructural properties due to variability in measured pole figures 2020Uncertainty-quantified parametrically homogenized constitutive models (UQ-PHCMs) for dual-phase α/β titanium alloys 2020Recent progress of uncertainty quantification in small-scale materials science 2017Uncertainty Quantification of Microstructural Properties due to Experimental Variations 2019Uncertainty Quantification for Ti-7Al Alloy Microstructure with an Inverse Analytical Model (AUQLin)

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Details

Year2022

Typearticle

Published2022-09-23

VenueComputational Materials Science

ISSN-L0927-0256

Languageen

Citations8

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.commatsci.2022.111775

openalex_workW4297026119

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