Gurtej Kanwar

TL;DR: In this paper, a Markov chain update scheme using a machine-learned flow-based generative model is proposed for Monte Carlo sampling in lattice field theories, which can be optimized (trained) to produce samples from a distribution approximating the desired Boltzmann distribution determined by the lattice action of the theory being studied.

TL;DR: This work defines a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge invariant by construction and demonstrates the application of this framework to U(1) gauge theory in two spacetime dimensions.

TL;DR: In this article, a flow-based sampling algorithm for lattice gauge theories that is gauge invariant by construction is presented. But this algorithm is restricted to two dimensions and cannot be used to sample distributions of single SU(N) variables.

TL;DR: This paper proposes and compares expressive and numerically stable flows on spaces with more complex geometries, such as tori or spheres, and builds recursively on the dimension of the space, starting from flows on circles, closed intervals or spheres.

TL;DR: Simit is a new language for physical simulations that lets the programmer view the system both as a linked data structure in the form of a hypergraph and as a set of global vectors, matrices, and tensors depending on what is convenient at any given time.