Frequency-adaptive multi-scale deep neural networks

Multi-scale deep neural networks (MscaleDNNs) with downing-scaling mapping have demonstrated superiority over traditional DNNs in approximating target functions characterized by
high frequency features. However, the performance of MscaleDNNs heavily depends on the
parameters in the downing-scaling mapping, which limits their broader application. In this
work, we establish a fitting error bound to explain why MscaleDNNs are advantageous for
approximating high frequency functions. Building on this insight, we construct a hybrid feature
embedding to enhance the accuracy and robustness of the downing-scaling mapping. To reduce
the dependency of MscaleDNNs on parameters in the downing-scaling mapping, we propose
frequency-adaptive MscaleDNNs, which adaptively adjust these parameters based on a posterior
error estimate that captures the frequency information of the fitted functions. Numerical
examples, including wave propagation and the propagation of a localized solution of the
Schrödinger equation with a smooth potential near the semi-classical limit, are presented. These
examples demonstrate that the frequency-adaptive MscaleDNNs improve accuracy by two to
three orders of magnitude compared to standard MscaleDNNs.