activity
20172021
most citedInformation Content of Hierarchical n-Point Polytope Functions for Quantifying and Reconstructing Disordered Systems

15 citations · 21 across the 4 of their papers we have counts for

collaborators

5 papers

physics.geo-ph20211 cited

AI-driven Bayesian inference of statistical microstructure descriptors from finite-frequency waves

Wouter Klessens, Ivan Vasconcelos, Yang Jiao

The ability to image materials at the microscale from long-wavelength wave data is a major challenge to the geophysical, engineering and medical fields. Here, we present a framewor…

cond-mat.stat-mech202015 cited

Information Content of Hierarchical n-Point Polytope Functions for Quantifying and Reconstructing Disordered Systems

Pei-En Chen, Wenxiang Xu, Yi Ren +1

Disordered systems are ubiquitous in physical, biological and material sciences. Examples include liquid and glassy states of condensed matter, colloids, granular materials, porous…

cond-mat.soft2020

Modeling Multi-Cellular Dynamics Regulated by ECM-Mediated Mechanical Communication via Active Particles with Polarized Effective Attraction

Yu Zheng, Qihui Fan, Christopher Eddy +4

Collective cell migration is crucial to many physiological and pathological processes. Recent experimental studies have indicated that the active traction forces generated by migra…

physics.app-ph2020

Microstructure Design of Low-Melting-Point Alloy (LMPA)/ Polymer Composites for Dynamic Dry Adhesion Tuning in Soft Gripping

Yaopengxiao Xu, Pei-En Chen, Wenxiang Xu +3

Tunable dry adhesion is a crucial mechanism in compliant manipulation. The gripping force, mainly originated from the van der Waals force between the adhesive composite and the obj…

physics.comp-ph20175 cited

Improving Direct Physical Properties Prediction of Heterogeneous Materials from Imaging Data via Convolutional Neural Network and a Morphology-Aware Generative Model

Ruijin Cang, Hechao Li, Hope Yao +2

Direct prediction of material properties from microstructures through statistical models has shown to be a potential approach to accelerating computational material design with lar…