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Calculating Quasi-Normal Modes of Schwarzschild Black Holes with Physics Informed Neural Networks
- Publication Year :
- 2024
-
Abstract
- 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.<br />Comment: 20 pages, 6 figures
- Subjects :
- General Relativity and Quantum Cosmology
Physics - Computational Physics
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2401.01440
- Document Type :
- Working Paper