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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2105.11309 |
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| _version_ | 1866911767989321728 |
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| author | Qin, Chenxin Liu, Ruhao Li, Maocai Li, Shengyuan Liu, Yi Zhou, Chichun |
| author_facet | Qin, Chenxin Liu, Ruhao Li, Maocai Li, Shengyuan Liu, Yi Zhou, Chichun |
| contents | Recent advances in solving ordinary differential equations (ODEs) with neural networks have been remarkable. Neural networks excel at serving as trial functions and approximating solutions within functional spaces, aided by gradient backpropagation algorithms. However, challenges remain in solving complex ODEs, including high-order and nonlinear cases, emphasizing the need for improved efficiency and effectiveness. Traditional methods have typically relied on established knowledge integration to improve problem-solving efficiency. In contrast, this study takes a different approach by introducing a new neural network architecture for constructing trial functions, known as ratio net. This architecture draws inspiration from rational fraction polynomial approximation functions, specifically the Pade approximant. Through empirical trials, it demonstrated that the proposed method exhibits higher efficiency compared to existing approaches, including polynomial-based and multilayer perceptron (MLP) neural network-based methods. The ratio net holds promise for advancing the efficiency and effectiveness of solving differential equations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2105_11309 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Efficiently Solving High-Order and Nonlinear ODEs with Rational Fraction Polynomial: the Ratio Net Qin, Chenxin Liu, Ruhao Li, Maocai Li, Shengyuan Liu, Yi Zhou, Chichun Machine Learning Numerical Analysis Recent advances in solving ordinary differential equations (ODEs) with neural networks have been remarkable. Neural networks excel at serving as trial functions and approximating solutions within functional spaces, aided by gradient backpropagation algorithms. However, challenges remain in solving complex ODEs, including high-order and nonlinear cases, emphasizing the need for improved efficiency and effectiveness. Traditional methods have typically relied on established knowledge integration to improve problem-solving efficiency. In contrast, this study takes a different approach by introducing a new neural network architecture for constructing trial functions, known as ratio net. This architecture draws inspiration from rational fraction polynomial approximation functions, specifically the Pade approximant. Through empirical trials, it demonstrated that the proposed method exhibits higher efficiency compared to existing approaches, including polynomial-based and multilayer perceptron (MLP) neural network-based methods. The ratio net holds promise for advancing the efficiency and effectiveness of solving differential equations. |
| title | Efficiently Solving High-Order and Nonlinear ODEs with Rational Fraction Polynomial: the Ratio Net |
| topic | Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2105.11309 |