Complex Physics-Informed Neural Network
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909642149330944 |
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| author | Si, Chenhao Yan, Ming Li, Xin Xia, Zhihong |
| author_facet | Si, Chenhao Yan, Ming Li, Xin Xia, Zhihong |
| contents | We propose compleX-PINN, a novel physics-informed neural network (PINN) architecture incorporating a learnable activation function inspired by the Cauchy integral theorem. By optimizing the activation parameters, compleX-PINN achieves high accuracy with just a single hidden layer. Empirically, we demonstrate that compleX-PINN solves high-dimensional problems that pose significant challenges for PINNs. Our results show that compleX-PINN consistently achieves substantially greater precision, often improving accuracy by an order of magnitude, on these complex tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04917 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Complex Physics-Informed Neural Network Si, Chenhao Yan, Ming Li, Xin Xia, Zhihong Machine Learning Artificial Intelligence We propose compleX-PINN, a novel physics-informed neural network (PINN) architecture incorporating a learnable activation function inspired by the Cauchy integral theorem. By optimizing the activation parameters, compleX-PINN achieves high accuracy with just a single hidden layer. Empirically, we demonstrate that compleX-PINN solves high-dimensional problems that pose significant challenges for PINNs. Our results show that compleX-PINN consistently achieves substantially greater precision, often improving accuracy by an order of magnitude, on these complex tasks. |
| title | Complex Physics-Informed Neural Network |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.04917 |