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• W3157426598 abstract "We present a Fourier neural network (FNN) that can be mapped directly to the Fourier decomposition. The choice of activation and loss function yields results that replicate a Fourier series expansion closely while preserving a straightforward architecture with a single hidden layer. The simplicity of this network architecture facilitates the integration with any other higher-complexity networks, at a data pre- or postprocessing stage. We validate this FNN on naturally periodic smooth functions and on piecewise continuous periodic functions. We showcase the use of this FNN for modeling or solving partial differential equations with periodic boundary conditions. The main advantages of the current approach are the validity of the solution outside the training region, interpretability of the trained model, and simplicity of use." @default.
• W3157426598 created "2021-05-10" @default.
• W3157426598 creator A5031844931 @default.
• W3157426598 creator A5071682789 @default.
• W3157426598 date "2020-05-27" @default.
• W3157426598 modified "2023-09-27" @default.
• W3157426598 title "Fourier Neural Networks as Function Approximators and Differential Equation Solvers" @default.
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• W3157426598 hasPublicationYear "2020" @default.
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