Julian M. Urban

According to our database, Julian M. Urban authored at least 11 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Practical applications of machine-learned flows on gauge fields.

Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2024

Applications of flow models to the generation of correlated lattice QCD ensembles.

Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2024

2023

Normalizing flows for lattice gauge theory in arbitrary space-time dimension.

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2023

2022

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

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

CoRR, 2022

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

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

CoRR, 2022

Flow-based density of states for complex actions.

Jan M. Pawlowski, Julian M. Urban

CoRR, 2022

Flow-based sampling in the lattice Schwinger model at criticality.

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

CoRR, 2022

2021

Flow-based sampling for fermionic lattice field theories.

Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

CoRR, 2021

2020

Reducing autocorrelation times in lattice simulations with generative adversarial networks.

Jan M. Pawlowski, Julian M. Urban

Mach. Learn. Sci. Technol., 2020

Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning.

Stefan Bluecher, Lukas Kades, Jan M. Pawlowski, Nils Strodthoff, Julian M. Urban

CoRR, 2020

2019

Spectral Reconstruction with Deep Neural Networks.

Lukas Kades, Jan M. Pawlowski, Alexander Rothkopf, Manuel Scherzer, Julian M. Urban, Sebastian Johann Wetzel, Nicolas Wink, Felix Ziegler

CoRR, 2019