STATISTICAL DESCRIPTION OF MICROSTRUCTURES
By Salvatore Torquato. 2002
253 citations recorded by the source corpus.
Subfields: Condensed Matter Physics, Materials Chemistry, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/99938c416d2257f7c6dd14d551934a02224dfbc0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Condensed Matter Physics
- R. Seright
- W. Brent Lindquist
- Rohit Unni
- Kan Yao
- Masa Prodanovic
- Yuebing Zheng
- C. McVeigh
- Wing Kam Liu
In Materials Chemistry
- R. Seright
- W. Brent Lindquist
- Rohit Unni
- Kan Yao
- Masa Prodanovic
- Yuebing Zheng
- Albert C. To
- Xiaolei Yin
In Mechanics of Materials
- Wing Kam Liu
- Rohit Unni
- Kan Yao
- Yuebing Zheng
- Masa Prodanovic
- W. Brent Lindquist
- R. Seright
- Albert C. To
Citations in the corpus
- 3D image-based characterization of fluid displacement in a Berea core (2007, methodology): “properties will require computation over an ensemble of stochastically equivalent network models to establish both predictive means as well as expected variance in flow behavior [14,39,41,42].”
- Hyperuniform states of matter (2018, background): “In the special case of one dimension with a fundamental cell of length D, this formula reduces to relation (87) of Torquato and Stillinger [27]: σ2 N (R) = 2 π2 ∞∑ m=1 sin2(2πmR/D) m2 [ M + 2 M∑ j k cos[2πm(rk − r j)/D] ] , (70) where, without loss of generality, the particle positions are ordered s”
- Statistical volume element method for predicting microstructure–constitutive property relations (2008, background): “Various statistical descriptions have been developed to characterize and reconstruct models of material microstructures [11,22,32,38] that contain various micro-features such as particles, voids, grains and microcracks.”
- Generation of 3D representative volume elements for heterogeneous materials: A review (2018, background): “Cellular materials such as nanoporous materials, foams, bone or microemulsions are nonparticulate and, therefore, cannot be modeled as a distribution of cavities in a matrix [61].”
- Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale (2018, methodology): “procedure of quasi-random light-trapping nanostructures for absorption enhancement [95]. Specifically, the processing patterns were represented statistically by the Fourier spectral density functions [96], which used only three variables to connect the structure and the optical property, making the ”