Physics-Informed AI, AI for Physics: From Precision Cosmology to Accelerated Discovery

Abstract

Deep generative models are emerging as powerful tools for solving physics problems, enabling new approaches to inference, sampling, anomaly detection, and signal reconstruction. In the first part of the talk, I will discuss how generative models, designed with physical principles such as symmetry and multi-scale structure, can be used to construct likelihood functions for cosmological inference at the field level, extracting 3–5× the information from weak gravitational lensing compared to traditional two-point statistics. This framework also enables anomaly detection of model misspecification and enhances interpretability through sample generation. I will also show that these physics-informed generative models apply beyond cosmology, including sampling lattice models in statistical physics and anomaly detection in collider physics. In the second part, I will discuss how generative models can be used to construct physically informed priors for inverse problems. This framework enables image reconstruction from intensity interferometry, opening a path to imaging supermassive black hole accretion disks. If time permits, I will also talk about how the same ideas apply to the gravitational wave background analysis from pulsar timing arrays, reconciling an apparent inconsistency with astrophysical predictions.