Search Results for author: Julian M. Urban

Found 8 papers, 1 papers with code

Aspects of scaling and scalability for flow-based sampling of lattice QCD

no code implementations14 Nov 2022 Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing.

Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions

no code implementations18 Jul 2022 Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban

This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant.

Flow-based density of states for complex actions

no code implementations2 Mar 2022 Jan M. Pawlowski, Julian M. Urban

In this proof-of-principle study, we demonstrate our method in the context of two-component scalar field theory where the $O(2)$ symmetry is explicitly broken by an imaginary external field.

Numerical Integration

Flow-based sampling in the lattice Schwinger model at criticality

no code implementations23 Feb 2022 Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

In this work, we provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass.

Flow-based sampling for fermionic lattice field theories

no code implementations10 Jun 2021 Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact.

Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning

no code implementations3 Mar 2020 Stefan Bluecher, Lukas Kades, Jan M. Pawlowski, Nils Strodthoff, Julian M. Urban

Machine learning has the potential to aid our understanding of phase structures in lattice quantum field theories through the statistical analysis of Monte Carlo samples.

BIG-bench Machine Learning Representation Learning

Spectral Reconstruction with Deep Neural Networks

no code implementations10 May 2019 Lukas Kades, Jan M. Pawlowski, Alexander Rothkopf, Manuel Scherzer, Julian M. Urban, Sebastian J. Wetzel, Nicolas Wink, Felix P. G. Ziegler

We explore artificial neural networks as a tool for the reconstruction of spectral functions from imaginary time Green's functions, a classic ill-conditioned inverse problem.

Bayesian Inference Spectral Reconstruction

Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks

1 code implementation8 Nov 2018 Julian M. Urban, Jan M. Pawlowski

Short autocorrelation times are essential for a reliable error assessment in Monte Carlo simulations of lattice systems.

High Energy Physics - Lattice Computational Physics

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