Description
Chair: François Lanusse
High dimensional sampling from a known target distribution is a ubiquitous problem across many fields of science and engineering. In astrophysics it is commonly used for Bayesian data analysis, and a few applications in Machine Learning are Bayesian Neural Networks, energy models and diffusion models. Many of the best known samplers have been inspired by physics ideas such as detailed balance, Langevin and Hamiltonian dynamics. Here I will describe two new physics inspired gradient based samplers called MicroCanonical Hamiltonian and Langevin Monte Carlo (MCHMC, MCLMC), and present some of their applications: 1) high dimensional sampling of initial conditions of our universe 2) field level inference of gravitational lensing of CMB 3) statistical physics and lattice QCD 4) Molecular Dynamics. On many of the applications MCLMC outperforms HMC by 1-3 orders of magnitude. I will also discuss the ongoing work with these samplers such as ensembling, stochastic gradients and optimization.
