12 citations · 15 across the 6 of their papers we have counts for
6 papers
Learning Metamaterial Eigenmodes with Wavelet-Encoded Fourier Neural Operators
Han Zhang, Alexander Ogren, Cynthia Rudin +2
Machine learning surrogates based on neural operators have shown broad applicability in solving forward PDE problems. However, eigenvalue problems, in which an eigenparameter and o…
Graded anisotropic metamaterials for elastic wave mode conversion
Jagannadh Boddapati, Jihoon Ahn, Alexander C Ogren +1
Efficient transmission of elastic waves across interfaces is central to several applications, including medical imaging, seismic isolation, and transducer design. Interfaces with a…
Visual Surface Wave Elastography: Revealing Subsurface Physical Properties via Visible Surface Waves
Alexander C. Ogren, Berthy T. Feng, Jihoon Ahn +2
Wave propagation on the surface of a material contains information about physical properties beneath its surface. We propose a method for inferring the thickness and stiffness of a…
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning
Mary V. Bastawrous, Zhi Chen, Alexander C. Ogren +3
Manipulating the dispersive characteristics of vibrational waves is beneficial for many applications, e.g., high-precision instruments. architected hierarchical phononic materials…
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…
Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video
Berthy T. Feng, Alexander C. Ogren, Chiara Daraio +1
An object's interior material properties, while invisible to the human eye, determine motion observed on its surface. We propose an approach that estimates heterogeneous material p…