Description
Chair: Shirley Ho
As we move towards the next generation of cosmological surveys, observational cosmology has reached an interesting stage in which making analytic predictions for the likelihood of the cosmological signal becomes intractable. Instead, physical models in the form of simulations offer an avenue to model the data in all of its complexity, but until very recently using such models to estimate physical fields and parameters remained an open problem.
In this talk, I will discuss two possible points of view on simulators, depending on whether they are “black-box” or “open-box” models, and the different methodologies and strategies which may be applied in each case to use these physical models within a Bayesian inference context.
In the case of black-box simulations (which can only be sampled from), I will discuss applications of deep generative models as a practical way to manipulate implicit distributions within a larger Bayesian framework. I will provide examples in particular of using a simulation-based prior captured by a neural score estimator to sample high-dimensional posterior distributions of cosmological fields.
In the case of open-box simulations, which can be seen as differentiable probabilistic models, with an explicit joint log probability, I will discuss strategies and challenges for building large scale differentiable physical models of the Universe touching in particular on distributed differentiable N-body solvers and building accelerated hybrid physical/ml simulations leveraging neural ODE methodologies.
