APRIL augments neural network loss with auxiliary physical redundancy terms to reshape the optimization landscape while preserving the true minimum, yielding up to 10x better accuracy in noise-free gravitational wave parameter estimation for chirp mass, total mass, and mass ratio.
Overview of KAGRA: Calibration, detector charac- terization, physical environmental monitors, and the geophysics interferometer,
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APRIL: Auxiliary Physically-Redundant Information in Loss -- A physics-informed framework for parameter estimation with a gravitational-wave case study
APRIL augments neural network loss with auxiliary physical redundancy terms to reshape the optimization landscape while preserving the true minimum, yielding up to 10x better accuracy in noise-free gravitational wave parameter estimation for chirp mass, total mass, and mass ratio.