The rapid growth of both astrophysical data and simulation capabilities is creating a new opportunity. Instead of being tied to summary statistics (like correlation functions and power spectra), we can begin to connect simulations and observations directly at the field level.<\/p>\n
In this talk, I will present a framework for field-level, multi-probe inference built around differentiable simulations, where gradients can be propagated through the forward model itself. I will focus on diffhydro, a differentiable hydrodynamics framework written in JAX that combines modern multiphysics solvers with end-to-end automatic differentiation. Starting from simple dark-matter models, we can incrementally add more realistic physics, including turbulence, radiative heating and cooling, and self-gravity, while retaining the ability to optimize directly through the simulation.<\/p>\n
This makes it possible to connect observations to initial conditions (i.e. latent fields), physical parameters, and unresolved processes in a unified way. I will show how these ideas open the door to reconstructing the history of the Universe and how the same framework can be a platform for embedded machine learning models for additional acceleration and new physics discovery.<\/p>\n","protected":false},"featured_media":0,"template":"","tags":[],"seminar_category":[154],"acf":{"s_now_accepting":true,"s_date_order":"2026-03-24 17:00:00","s_date_end":"2026-03-24 18:00:00","s_date_text":"17:00 - 18:00","s_text":"Benjamin Horowitz (Kavli IPMU)","s_place":"ES635 + Zoom","s_place_other":"","s_categoryother":"","s_poster":"","s_poster2":"","s_slide":""},"yoast_head":"\n