KAAI Workshop - AI Methods for Turbulence and Early-Universe Signals

Turbulence appears across astrophysics and cosmology, from magnetized plasmas in galaxies and the interstellar medium to the primordial plasma of the early universe. Early-universe processes such as first-order phase transitions and primordial magnetic fields can drive turbulence, which may leave observable imprints in stochastic gravitational-wave backgrounds and in cosmic magnetic fields. Modeling these systems requires expensive simulations over a wide range of scales, and connecting them to data is a hard inverse problem. This workshop brings together researchers in turbulence, early-universe physics, and machine learning to discuss how AI methods can help with simulation, inference, and signal detection in this area. AI methods for turbulence: surrogate models, emulators, and learned representations of hydrodynamic and magnetohydrodynamic turbulence across scales. Early-universe signals: modeling and inference of signatures of early-universe physics, including gravitational-wave backgrounds and primordial magnetic fields. This is a Keystone Astronomy & AI (KAAI) Fellows Program workshop, sponsored by the Simons Foundation Targeted Grant to Institutes.