5 papers
A Multi-Level Machine Learning Framework for Inverse Scattering Problems with Multi-Frequency Data
Yi Liu, Yanzhao Cao, Junshan Lin +1
In this work, we propose a multi-level machine learning framework for solving inverse scattering problems with multi-frequency data. The multi-level neural network is built along t…
Fourier Multi-Component and Multi-Layer Neural Networks: Unlocking High-Frequency Potential
Shijun Zhang, Hongkai Zhao, Yimin Zhong +1
The architecture of a neural network and the choice of its activation function are both fundamental to its performance. Equally important is ensuring that these two elements are we…
Structured and Balanced Multi-Component and Multi-Layer Neural Networks
Shijun Zhang, Hongkai Zhao, Yimin Zhong +1
In this work, we propose a balanced multi-component and multi-layer neural network (MMNN) structure to accurately and efficiently approximate functions with complex features, in te…
Why Shallow Networks Struggle to Approximate and Learn High Frequencies
Shijun Zhang, Hongkai Zhao, Yimin Zhong +1
In this work, we present a comprehensive study combining mathematical and computational analysis to explain why a two-layer neural network struggles to handle high frequencies in b…
Transport models for wave propagation in scattering media with nonlinear absorption
Joseph Kraisler, Wei Li, Kui Ren +2
This work considers the propagation of high-frequency waves in highly-scattering media where physical absorption of a nonlinear nature occurs. Using the classical tools of the Wign…