Calculating Quasi-Normal Modes of Schwarzschild Black Holes with Physics Informed Neural Networks
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| Format: | Preprint |
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2024
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| _version_ | 1866917558016278528 |
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| author | Patel, Nirmal Aykutalp, Aycin Laguna, Pablo |
| author_facet | Patel, Nirmal Aykutalp, Aycin Laguna, Pablo |
| contents | Machine learning, particularly neural networks, has rapidly permeated most activities and work where data has a story to tell. Recently, deep learning has started to be used for solving differential equations with input from physics, also known as Physics Informed Neural Networks (PINNs). We present a study showing the efficacy of PINNs for solving the Zerilli and the Regge-Wheeler equations in the time domain to calculate the quasi-normal oscillation modes of a Schwarzschild black hole. We compare the extracted modes with those obtained with finite difference methods. Although the PINN results are competitive, with a few percent differences in the quasi-normal modes estimates relative to those computed with finite difference methods, the real power of PINNs will emerge when applied to large dimensionality problems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_01440 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Calculating Quasi-Normal Modes of Schwarzschild Black Holes with Physics Informed Neural Networks Patel, Nirmal Aykutalp, Aycin Laguna, Pablo General Relativity and Quantum Cosmology Computational Physics Machine learning, particularly neural networks, has rapidly permeated most activities and work where data has a story to tell. Recently, deep learning has started to be used for solving differential equations with input from physics, also known as Physics Informed Neural Networks (PINNs). We present a study showing the efficacy of PINNs for solving the Zerilli and the Regge-Wheeler equations in the time domain to calculate the quasi-normal oscillation modes of a Schwarzschild black hole. We compare the extracted modes with those obtained with finite difference methods. Although the PINN results are competitive, with a few percent differences in the quasi-normal modes estimates relative to those computed with finite difference methods, the real power of PINNs will emerge when applied to large dimensionality problems. |
| title | Calculating Quasi-Normal Modes of Schwarzschild Black Holes with Physics Informed Neural Networks |
| topic | General Relativity and Quantum Cosmology Computational Physics |
| url | https://arxiv.org/abs/2401.01440 |