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Late-Time Alleviation of the Hubble Tension in CPL Cosmology with Massive Neutrinos via Bayesian Physics-Informed Neural Networks
We present a Bayesian analysis of the Hubble constant using Physics-Informed Neural Networks (PINNs), applied to the standard wCDM model and its dynamical extension via the Chevallier--Polarski--Linder (CPL) parametrization, with and without a free summed neutrino mass $Σm_ν$. Embedding the
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