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Ting-Ting Gao

Publications and source records attributed to Ting-Ting Gao.

2 recordsLinked to original sources

Scale-Rich Network-Based Metamaterials

Materials, at their essence, are networks defined by homogeneity: uniform bonds, fixed thicknesses, and discrete length scales. Mechanical metamaterials, while representing structurally more diverse microstructures, remain defined by the homogeneity of their unit cells, pore sizes, or repeating features. In contrast, as network science has revealed, real-world and biological systems -- from the Internet to the brain -- derive their function from broad, multiscale variability in connectivity and link length. Here, we introduce Scale-Rich (SR) metamaterials, a design framework that embeds network heterogeneity into mechanical metamaterials, achieving order-of-magnitude heterogeneity in ligament lengths, thicknesses, and connectivity. Governed by only two parameters, SR networks span orders of magnitude in structural features, overcoming prior constraints in metamaterial design. Translating these network models into physically realizable materials, we use simulations and experiments to show that SR metamaterials exhibit properties inaccessible to traditional single-scale systems, including highly tunable elastic anisotropy, delocalized nonlinear deformation with high energy absorption, and programmable acoustic wave control. This network-science-based paradigm establishes a minimal yet universal framework for engineering multifunctional materials whose mechanical and acoustic behavior emerge directly from scale diversity itself.

cond-mat.soft

Autonomous inference of complex network dynamics from incomplete and noisy data

The availability of empirical data that capture the structure and behavior of complex networked systems has been greatly increased in recent years, however a versatile computational toolbox for unveiling a complex system's nodal and interaction dynamics from data remains elusive. Here we develop a two-phase approach for autonomous inference of complex network dynamics, and its effectiveness is demonstrated by the tests of inferring neuronal, genetic, social, and coupled oscillators dynamics on various synthetic and real networks. Importantly, the approach is robust to incompleteness and noises, including low resolution, observational and dynamical noises, missing and spurious links, and dynamical heterogeneity. We apply the two-phase approach to inferring the early spreading dynamics of H1N1 flu upon the worldwide airline network, and the inferred dynamical equation can also capture the spread of SARS and COVID-19 diseases. These findings together offer an avenue to discover the hidden microscopic mechanisms of a broad array of real networked systems.

physics.soc-ph