The concordance model of cosmology has been remarkably successful in explaining a wide range of observational data. However, persistent theoretical challenges remain, such as the value of the cosmological constant (Λ), alongside significant discrepancies in Hubble constant estimates and tensions related to the large-scale structure, specifically the σ8 parameter. These issues suggest that new physics may be required to fully understand the cosmos. Several avenues are actively being explored to address these challenges, including models based on quantum gravity, such as Asymptotically Safe Quantum Gravity, and potential modifications to General Relativity. In parallel, the unprecedented accumulation of observational data, combined with the rise of efficient and interpretable Deep Learning algorithms, presents new opportunities. Of particular interest are Physics-Informed Neural Networks, which offer promising approaches for reconstructing fundamental quantities directly from the data.
Videoconference via https://us02web.zoom.us/j/87847595792