Physics-Informed Network Models: a Data Science Approach to Metal Design


Functional graded materials (FGM) allow for reconciliation of conflicting design constraints at different locations in the material. This optimization requires a priori knowledge of how different architectural measures are interdependent and combine to control material performance. In this work, an aluminum FGM was used as a model system to present a new network modeling approach that captures the relationship between design parameters and allows an easy interpretation. The approach, in an un-biased manner, successfully captured the expected relationships and was capable of predicting the hardness as a function of composition.

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The material for this study was provided by the Alcoa/Arconic Alloy Technology Division. This work was funded by the Army Research Office Short Term Innovative Research program: W911NF-14-0549. The data and R-analytics code utilized for the paper can be found at

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Correspondence to Jennifer L. W. Carter.

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Verma, A.K., French, R.H. & Carter, J.L.W. Physics-Informed Network Models: a Data Science Approach to Metal Design. Integr Mater Manuf Innov 6, 279–287 (2017).

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  • Metal design
  • Network models
  • Optimization
  • Functional gradient materials