In this paper, we propose a multiscale DeepONet to represent nonlinear operator between Banach spaces of highly oscillatory continuous functions. The multiscale deep neural network (DNN) utilizes a multiple scaling technique to convert high frequency function to lower frequency functions before using a DNN to learn a specific range of frequency of the function. The multi-scale concept is integrated into the DeepONet which is based on a universal approximation theory of nonlinear operators. The resulting multi-scale DeepONet is shown to be effective to represent building seismic response operator which maps oscillatory seismic excitation to the oscillatory building responses.
@article{arxiv.2111.04860,
title = {Multiscale DeepONet for Nonlinear Operators in Oscillatory Function Spaces for Building Seismic Wave Responses},
author = {Lizuo Liu and Wei Cai},
journal= {arXiv preprint arXiv:2111.04860},
year = {2021}
}