Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics

By Noah H. Paulson, M. Priddy, David L. McDowell, Surya R. Kalidindi. 2017

164 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/a0f53e80d5211542ea9f4e43d284f46efc108fa9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Surya R. Kalidindi

In Mechanical Engineering

1. Surya R. Kalidindi
2. Franz Roters
3. Dierk Raabe
4. David L. McDowell
5. Philip Eisenlohr
6. T. Bieler

In Mechanics of Materials

1. Y. Yabansu
2. Surya R. Kalidindi
3. Franz Roters
4. Dierk Raabe
5. Philip Eisenlohr
6. T. Bieler
7. David L. McDowell

Citations in the corpus

• Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets (2018, methodology): “This approach has been shown to synergistically combine the respective merits of both the analytical and numerical approaches described earlier, and provide remarkably accurate, low computational cost, structure-property linkages for low to medium contrast composites [7,9,11,15,18,38].”