6 papers
Wide-band butterfly network: stable and efficient inversion via multi-frequency neural networks
Matthew Li, Laurent Demanet, Leonardo Zepeda-Núñez
We introduce an end-to-end deep learning architecture called the wide-band butterfly network (WideBNet) for approximating the inverse scattering map from wide-band scattering data.…
Efficient Long-Range Convolutions for Point Clouds
Yifan Peng, Lin Lin, Lexing Ying +1
The efficient treatment of long-range interactions for point clouds is a challenging problem in many scientific machine learning applications. To extract global information, one us…
Learning the mapping : the cost of finding the needle in a haystack
Jiefu Zhang, Leonardo Zepeda-Núñez, Yuan Yao +1
The task of using machine learning to approximate the mapping with seems to be a trivial one. Given the knowledge of the separa…
L-Sweeps: A scalable, parallel preconditioner for the high-frequency Helmholtz equation
Matthias Taus, Leonardo Zepeda-Núñez, Russell J Hewett +1
We present the first fast solver for the high-frequency Helmholtz equation that scales optimally in parallel, for a single right-hand side. The L-sweeps approach achieves this scal…
Efficient hybridization fitting for dynamical mean-field theory via semi-definite relaxation
Carlos Mejuto-Zaera, Leonardo Zepeda-Núñez, Michael Lindsey +3
We introduce a nested optimization procedure using semi-definite relaxation for the fitting step in Hamiltonian-based cluster dynamical mean-field theory (DMFT) methodologies. We s…
A multiscale neural network based on hierarchical matrices
Yuwei Fan, Lin Lin, Lexing Ying +1
In this work we introduce a new multiscale artificial neural network based on the structure of -matrices. This network generalizes the latter to the nonlinear case by…