| Autor: |
Guo, Yixiao, Ming, Pingbing |
| Rok vydání: |
2023 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
We present a novel deep learning method for computing eigenvalues of the fractional Schr\"odinger operator. Our approach combines a newly developed loss function with an innovative neural network architecture that incorporates prior knowledge of the problem. These improvements enable our method to handle both high-dimensional problems and problems posed on irregular bounded domains. We successfully compute up to the first 30 eigenvalues for various fractional Schr\"odinger operators. As an application, we share a conjecture to the fractional order isospectral problem that has not yet been studied. |
| Databáze: |
arXiv |
| Externí odkaz: |
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