Speaker
Description
The physics-informed renormalisation group describes general reparametrisation of given data distributions. It provides an analytic flow in terms of the distribution itself and the differential reparametrisation, the physics-informed kernel. From the perspective of quantum field theory, this flow can be understood as a general non-linear coarse graining procedure. From the perspective of Machine learning architectures, this flow provides a general layerwise propagation with a general activation function. Hence, it encompasses generative architectures such as normalising flows, PINNs or diffusion models as well as traditional sampling approaches such as sampling with, HMC, (complex) Langevin equations and on Lefschetz thimbles as special cases, endowed with an additional key property, the weight preservation of the map.
I will provide a short introduction to the approach, concentrating on the key property of weight-preservation of the map with the physics-informed kernel. It can be either used as a standard sampling algorithm or as an additional generative structure in generative NNs. The talk closes with a brief discussion of its implementation for sampling real distributions as well as complex distributions with a sign problem.
More details and applications will be provided in the talks of Friederike Ihssen and Renzo Kapust.