Browsing by Subject "Physics-informed"

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Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis

Guo, Hongwei; Zhuang, Xiaoying; Chen, Pengwan; Alajlan, Naif; Rabczuk, Timon (London : Springer, 2022)

In this work, we present a deep collocation method (DCM) for three-dimensional potential problems in non-homogeneous media. This approach utilizes a physics-informed neural network with material transfer learning reducing ...
Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials

Guo, Hongwei; Zhuang, Xiaoying; Fu, Xiaolong; Zhu, Yunzheng; Rabczuk, Timon (Berlin ; Heidelberg : Springer, 2023)

We present a physics-informed deep learning model for the transient heat transfer analysis of three-dimensional functionally graded materials (FGMs) employing a Runge–Kutta discrete time scheme. Firstly, the governing ...
Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media

Guo, Hongwei; Zhuang, Xiaoying; Chen, Pengwan; Alajlan, Naif; Rabczuk, Timon (London : Springer, 2022)

We present a stochastic deep collocation method (DCM) based on neural architecture search (NAS) and transfer learning for heterogeneous porous media. We first carry out a sensitivity analysis to determine the key ...

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