Hybrid Physical-Neural Simulator for Fast Cosmological Hydrodynamics

Arne Thomsen, Tilman Tröster, Francois Lanusse

Abstract:

Cosmological field-level inference requires differentiable forward models that solve
the challenging dynamics of gas and dark matter under hydrodynamics and gravity.
We propose a hybrid approach where gravitational forces are computed using a
differentiable particle-mesh solver, while the hydrodynamics are parametrized
by a neural network that maps local quantities to an effective pressure field. We
demonstrate that our method improves upon alternative approaches, such as an
Enthalpy Gradient Descent baseline, both at the field and summary-statistic level.
The approach is furthermore highly data efficient, with a single reference simulation
of cosmological structure formation being sufficient to constrain the neural pressure
model. This opens the door for future applications where the model is fit directly
to observational data, rather than a training set of simulations.