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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.01440 |
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| _version_ | 1866912637082664960 |
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| author | Lin, Zhongshuo Ma, Qingkui Xie, Hehu Yin, Xiaobo |
| author_facet | Lin, Zhongshuo Ma, Qingkui Xie, Hehu Yin, Xiaobo |
| contents | In this paper, we propose a novel machine learning method based on adaptive tensor neural network subspace to solve linear time-fractional diffusion-wave equations and nonlinear time-fractional partial integro-differential equations. In this framework, the tensor neural network and Gauss-Jacobi quadrature are effectively combined to construct a universal numerical scheme for the temporal Caputo derivative with orders spanning $ (0,1)$ and $(1,2)$. Specifically, in order to effectively utilize Gauss-Jacobi quadrature to discretize Caputo derivatives, we design the tensor neural network function multiplied by the function $t^μ$ where the power $μ$ is selected according to the parameters of the equations at hand. Finally, some numerical examples are provided to validate the efficiency and accuracy of the proposed tensor neural network based machine learning method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01440 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solving Time-Fractional Partial Integro-Differential Equations Using Tensor Neural Network Lin, Zhongshuo Ma, Qingkui Xie, Hehu Yin, Xiaobo Machine Learning In this paper, we propose a novel machine learning method based on adaptive tensor neural network subspace to solve linear time-fractional diffusion-wave equations and nonlinear time-fractional partial integro-differential equations. In this framework, the tensor neural network and Gauss-Jacobi quadrature are effectively combined to construct a universal numerical scheme for the temporal Caputo derivative with orders spanning $ (0,1)$ and $(1,2)$. Specifically, in order to effectively utilize Gauss-Jacobi quadrature to discretize Caputo derivatives, we design the tensor neural network function multiplied by the function $t^μ$ where the power $μ$ is selected according to the parameters of the equations at hand. Finally, some numerical examples are provided to validate the efficiency and accuracy of the proposed tensor neural network based machine learning method. |
| title | Solving Time-Fractional Partial Integro-Differential Equations Using Tensor Neural Network |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.01440 |