Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials

By Zeliang Liu, Miguel A. Bessa, Wing Kam Liu. 2016

441 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/885d72895e52a8e1b58c67a922af30f525c7a1dd

How researchers see this paper

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

In Computational Theory and Mathematics

1. Qizhi He
2. Jiun-Shyan Chen
3. Mark Fleming
4. Zeliang Liu
5. Wing Kam Liu
6. Wei Chen
7. Ramin Bostanabad
8. Daniel W. Apley

In Mechanics of Materials

1. Qizhi He
2. Jiun-Shyan Chen
3. Wing Kam Liu
4. Mark Fleming
5. Zeliang Liu
6. C. Brinson
7. Wei Chen
8. Ramin Bostanabad

Citations in the corpus

• A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database (2019, methodology): “[37] Z.”
• A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality (2017, methodology): “The high-fidelity RVE is analyzed by the finite element method using a mesh of 512,000 elements that was shown previously [46] to lead to similar results as a coarser mesh.”
• Microstructural material database for self-consistent clustering analysis of elastoplastic strain softening materials (2018, methodology): “The self-consistent clustering analysis (SCA) recently proposed by Liu et al. (2016) provides an effective way of developing a microstructural database based on a clustering algorithm and the Lippmann-Schwinger integral equation, which enables an efficient and accurate prediction of nonlinear…”