Meshfree black hole toy evolution and excision using artificial neural networks

Authors

  • Lyle Kenneth M. Geraldez ⋅ PH National Institute of Physics, University of the Philippines Diliman
  • Michael Francis Ian G. Vega II ⋅ PH National Institute of Physics, University of the Philippines Diliman

Abstract

We present the use of physics-informed neural networks (PINNs) for handling singularities in the context black hole singularity evolution by offering meshfree alternatives for solving partial differential equations (PDEs). This was done by implementing a spatiotemporal excision surface across the entire spacetime domain rather than iteratively excising grid points. We demonstrate a toy model of black hole evolution using a simplified form of extended conformal thin sandwich (XCTS) initial data fed into a 1D wave equation as an evolution proxy. While PINNs can learn the asymptotic behavior outside the excised surface, it failed to learn the behavior near it. Although such an implementation has a large room for improvement especially when considering the complexity of NR applications, PINN-based numerical solver piggy-backs on the advancement of neural network architectures which may lay the possibility for feasible hybrid numerical solvers.

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Issue

Article ID

SPP-2023-PB-23

Section

Poster Session B (Complex Systems, Simulations, and Theoretical Physics)

Published

2023-07-10

How to Cite

[1]
LKM Geraldez and MFIG Vega, Meshfree black hole toy evolution and excision using artificial neural networks, Proceedings of the Samahang Pisika ng Pilipinas 41, SPP-2023-PB-23 (2023). URL: https://proceedings.spp-online.org/article/view/SPP-2023-PB-23.