Speaker
Description
Cosmological simulations are too computationally expensive to support exhaustive scans over dark matter parameter space. Generative models, including diffusion models and flow models, offer a promising route to simulation emulation using existing suites of numerical simulations. In this talk, I will describe diffusion and flow-based generative models as transport processes: they evolve probability densities through an abstract, high-dimensional data space according to equations closely related to continuity and flow equations derived from the boltzmann equation. This viewpoint provides a natural bridge to cosmological simulation, where density fields and particle distributions are likewise evolved under physically constrained dynamics. I will show how this connection can be used to inject theoretical priors into generative emulators. I will demonstrate the approach on both density-field emulation and point-cloud representations of cosmological boxes, with the goal of building faster and more physically informed surrogates for exploring dark matter parameter space.