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

Biwei Dai explores generative models built around physical symmetries and multiscale structure. For cosmology, these models enable field-level likelihoods that extract more information from weak lensing than two-point statistics and expose model misspecification through generated samples. Related methods address lattice sampling and collider anomalies. Physically informed generative priors also support inverse problems, including intensity-interferometry reconstruction of black-hole accretion disks and analysis of pulsar-timing gravitational-wave backgrounds.