Creating a Bayesian Digital Twin of Our Universe

Explore the development of a Bayesian digital twin of the Universe in this 47-minute conference talk from the 2025 Simons Collaboration on Learning the Universe Annual Meeting. Delve into cutting-edge computational cosmology techniques that combine Bayesian statistical methods with digital modeling to create sophisticated simulations of cosmic structures and evolution. Learn how researchers are leveraging probabilistic frameworks to quantify uncertainties in cosmological parameters while building comprehensive digital representations of our Universe. Discover the methodological approaches used to incorporate observational data into these models, understand the computational challenges involved in large-scale universe simulations, and examine how Bayesian inference enhances our ability to test cosmological theories against empirical evidence. Gain insights into the intersection of machine learning, statistical modeling, and astrophysics as applied to understanding fundamental questions about cosmic structure formation, dark matter distribution, and the evolution of the Universe from early times to the present day.