Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiscale DeepONet for Nonlinear Operators in Oscillatory Function
  Spaces for Building Seismic Wave Responses   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.

24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int