Reconstructing the generalized Barrow holographic dark energy with physics-informed neural networks
Barrow holographic dark energy connects cosmic acceleration with possible quantum-gravitational deformations of horizon entropy, encoded in the Barrow exponent $Δ$. If such effects are scale dependent, however, there is no fundamental reason for $Δ$ to remain constant throughout cosmic history. In this work we reconstruct $Δ(z)$ directly from observations, without assuming any particular functional form, using the Cosmo-PINN physics-informed neural-network framework. The generalized Barrow holographic evolution equation is incorporated into the training, while PantheonPlus supernovae, DESI DR2 baryon acoustic oscillations and cosmic chronometers constrain the reconstruction. We find a mild and smooth redshift evolution, with the posterior mean favoring negative $Δ$ and this tendency becoming stronger when the Cepheid calibration is included. Nevertheless, $Δ=0$ and constant negative values remain compatible with current uncertainties. The reconstructed cosmology yields a viable late-time evolution, with $w_{\rm DE}$ close to $-1$ and the expected transition to accelerated expansion. Our results demonstrate that cosmological observations can directly probe the functional behavior of a quantity entering the underlying entropy law itself.