42 citations · 56 across the 5 of their papers we have counts for
5 papers
Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory
Yueqi Zhao, Michael M. Fogler, Yi-Zhuang You
We introduce RGFlow, a deep neural network-based real-space renormalization group (RG) framework tailored for continuum scalar field theories. Leveraging generative capabilities of…
Can A Neural Network Hear the Shape of A Drum?
Yueqi Zhao, Michael M. Fogler
We have developed a deep neural network that reconstructs the shape of a polygonal domain given the first hundred of its Laplacian eigenvalues. Having an encoder-decoder structure,…
Machine Learning for Optical Scanning Probe Nanoscopy
Xinzhong Chen, Suheng Xu, Sara Shabani +7
The ability to perform nanometer-scale optical imaging and spectroscopy is key to deciphering the low-energy effects in quantum materials, as well as vibrational fingerprints in pl…
Deep-Learning-Aided Extraction of Optical Constants in Scanning Near-Field Optical Microscopy
Yueqi Zhao, Xinzhong Chen, Ziheng Yao +2
Scanning near-field optical microscopy is one of the most effective techniques for spectroscopy of nanoscale systems. However, inferring optical constants from the measured near-fi…
Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy
Xinzhong Chen, Ziheng Yao, Suheng Xu +11
The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where t…