A separable Fourier-feature neural network with learnable frequencies and a three-level frequency sampler is reported to solve high-frequency PDEs with far fewer parameters than vanilla PINNs.
The Helmholtz equation with uncertainties in the wavenumber
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abstract
We investigate the Helmholtz equation with suitable boundary conditions and uncertainties in the wavenumber. Thus the wavenumber is modeled as a random variable or a random field. We discretize the Helmholtz equation using finite differences in space, which leads to a linear system of algebraic equations including random variables. A stochastic Galerkin method yields a deterministic linear system of algebraic equations. This linear system is high-dimensional, sparse and complex symmetric but, in general, not hermitian. We therefore solve this system iteratively with GMRES and propose two preconditioners: a complex shifted Laplace preconditioner and a mean value preconditioner. Both preconditioners reduce the number of iteration steps as well as the computation time in our numerical experiments.
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Separated-Variable Spectral Neural Networks: A Physics-Informed Learning Approach for High-Frequency PDEs
A separable Fourier-feature neural network with learnable frequencies and a three-level frequency sampler is reported to solve high-frequency PDEs with far fewer parameters than vanilla PINNs.