A PINN learns higher-order corrections to the TaylorT4 PN model from eight NR surrogate waveforms, reducing phase and amplitude errors in the inspiral while enforcing physical symmetries.
Marion, Generalization bounds for neural ordinary differential equations and deep residual networks (2023), arXiv:2305.06648 [stat.ML]
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Learning Post-Newtonian Corrections from Numerical Relativity
A PINN learns higher-order corrections to the TaylorT4 PN model from eight NR surrogate waveforms, reducing phase and amplitude errors in the inspiral while enforcing physical symmetries.