Simulating Multiscale Astrophysics to Understand Galaxy formation

Building genuinely a priori models of galaxy formation in a cosmological context is one of the grand challenges of modern astrophysics. Most large volume simulations of galaxy formation currently adopt phenomenological scaling relations to model "small scale" processes such as star formation, stellar feedback, and black hole formation, growth, and feedback, which limits their predictive power. I will present first results from the SMAUG collaboration, which aims to build a more fundamental understanding of the broad array of physical processes that shape galaxy formation, using custom multiscale numerical simulations, analytic and semi-analytic models, and machine learning. I will discuss the prospects for using the next generation of more physically grounded galaxy formation models to interpret the results from upcoming observational probes.