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- W4384404775 abstract "In this paper, we propose a new deep learning algorithm based on the physics-informed neural network (PINN) for solving the Cahn–Hilliard (CH) equations. We adopt the discrete time model of the PINN and use Runge–Kutta methods to discretize the time variable. We introduce a novel loss function and update it at each time step so that the computational cost can be significantly reduced compared to some existing methods. Numerical results of several one-dimensional and two-dimensional CH equations are presented to validate the efficiency and accuracy of the proposed method." @default.
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- W4384404775 date "2023-10-01" @default.
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- W4384404775 title "An adaptive discrete physics-informed neural network method for solving the Cahn–Hilliard equation" @default.
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- W4384404775 doi "https://doi.org/10.1016/j.enganabound.2023.06.031" @default.
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