A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems
This addresses the problem of efficiently learning high-frequency content in wave problems for researchers in computational physics and machine learning, representing a novel method rather than an incremental improvement.
The paper tackles the challenge of approximating high-frequency functions and solving wave equations by proposing a Phase Shift Deep Neural Network (PhaseDNN), which achieves uniform wideband convergence and successfully applies to high-frequency Helmholtz equations with demonstrated numerical results.
In this paper, we propose a phase shift deep neural network (PhaseDNN), which provides a uniform wideband convergence in approximating high frequency functions and solutions of wave equations. The PhaseDNN makes use of the fact that common DNNs often achieve convergence in the low frequency range first, and a series of moderately-sized DNNs are constructed and trained for selected high frequency ranges. With the help of phase shifts in the frequency domain, each of the DNNs will be trained to approximate the function's higher frequency content over a specific range at the the speed of convergence as in the low frequency range. As a result, the proposed PhaseDNN is able to convert high frequency learning to low frequency one, allowing a uniform learning to wideband functions. The PhaseDNN will then be applied to find the solution of high frequency wave equations in inhomogeneous media through both differential and integral equation formulations with least square residual loss functions. Numerical results have demonstrated the capability of the PhaseDNN in learning high frequency functions and oscillatory solutions of interior and exterior Helmholtz equations.