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• W3186073110 abstract "In this work, we use an explainable convolutional neural network (NLS-Net) to solve an inverse problem of the nonlinear Schrodinger equation, which is widely used in fiber-optic communications. The landscape and minimizers of the non-convex loss function of the learning problem are studied empirically. It provides a guidance for choosing hyper-parameters of the method. The estimation error of the optimal solution is discussed in terms of expressive power of the NLS-Net and data. Besides, we compare the performance of several training algorithms that are popular in deep learning. It is shown that one can obtain a relatively accurate estimate of the considered parameters using the proposed method. The study provides a natural framework of solving inverse problems of nonlinear partial differential equations with deep learning." @default.
• W3186073110 created "2021-08-02" @default.
• W3186073110 creator A5015422395 @default.
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• W3186073110 date "2021-07-19" @default.
• W3186073110 modified "2023-10-16" @default.
• W3186073110 title "Inverse Problem of Nonlinear Schrödinger Equation as Learning of Convolutional Neural Network." @default.
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• W3186073110 hasPublicationYear "2021" @default.
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