activity
20052023
most citedComposite 3D-printed meta-structures for low frequency and broadband vibration absorption

485 citations · 5.2k across the 72 of their papers we have counts for

collaborators

72 papers

physics.med-ph2023★ 1 cited

AI-aided Geometric Design of Anti-infection Catheters

Tingtao Zhou, Xuan Wan, Daniel Zhengyu Huang +6

Bacteria can swim upstream due to hydrodynamic interactions with the fluid flow in a narrow tube, and pose a clinical threat of urinary tract infection to patients implanted with c…

cond-mat.mtrl-sci2023★ 16 cited

Single-test evaluation of directional elastic properties of anisotropic structured materials

Jagannadh Boddapati, Moritz Flaschel, Siddhant Kumar +2

When the elastic properties of structured materials become direction-dependent, the number of their descriptors increases. For example, in two-dimensions, the anisotropic behavior…

physics.app-ph2023★ 3 cited

Experimental Evidence of Amplitude-Dependent Surface Wave Dispersion via Nonlinear Contact Resonances

Setare Hajarolasvadi, Paolo Celli, Brian L. Kim +2

In this letter, we provide an experimental demonstration of amplitude-dependent dispersion tuning of surface acoustic waves interacting with nonlinear resonators. Leveraging the si…

physics.app-ph2022★ 16 cited

Dynamics of Time-Modulated, Nonlinear Phononic Lattices

Brian L. Kim, Christoper Chong, Setare Hajarolasvadi +2

The propagation of acoustic and elastic waves in time-varying, spatially homogeneous media can exhibit different phenomena when compared to traditional spatially-varying, temporall…

physics.app-ph2022

Capacitive temperature sensing via displacement amplification

Semih Taniker, Vincenzo Costanza, Paolo Celli +1

We propose the realization of capacitive temperature sensors based on the concept of displacement amplification. Our design features two high coefficient of thermal expansion (CTE)…

cs.LG2021★ 2 cited

How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning

Zhi Chen, Alexander Ogren, Chiara Daraio +2

Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems. However, past work has usuall…