Julian M. Urban

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Jan M. PawlowskiUniversität HeidelbergE-mailová adresa ověřena na: thphys.uni-heidelberg.de
Phiala ShanahanMassachusetts Institute of TechnologyE-mailová adresa ověřena na: mit.edu
Denis L. BoydaIAIFI Fellow, MITE-mailová adresa ověřena na: mit.edu
Daniel HackettPostdoc, FermilabE-mailová adresa ověřena na: fnal.gov
Gurtej KanwarUniversity of EdinburghE-mailová adresa ověřena na: ed.ac.uk
Michael S. AlbergoHarvard UniversityE-mailová adresa ověřena na: fas.harvard.edu
Sébastien RacanièreResearch Scientist, DeepMindE-mailová adresa ověřena na: google.com
Kyle CranmerUniversity of Wisconsin-MadisonE-mailová adresa ověřena na: wisc.edu
Nicolas WinkTU DarmstadtE-mailová adresa ověřena na: tu-darmstadt.de
Lukas KadesPostDoc, Universität HeidelbergE-mailová adresa ověřena na: thphys.uni-heidelberg.de
Savvas ZafeiropoulosCentre de Physique Théorique, CNRS and Aix Marseille UniversityE-mailová adresa ověřena na: cpt.univ-mrs.fr
Dr. Alexander RothkopfProfessor at Korea UniversityE-mailová adresa ověřena na: korea.ac.kr
Sebastian Johann WetzelPerimeter InstituteE-mailová adresa ověřena na: perimeterinstitute.ca
Jan HorakInstitut für Theoretische Physik, Universität HeidelbergE-mailová adresa ověřena na: thphys.uni-heidelberg.de
José Rodriguez-Quintero (ORCID: 000...Universidad de Huelva (RoR:03a1kt624)E-mailová adresa ověřena na: dfaie.uhu.es
Stefan BlücherPhD at BIFOLD (TU Berlin)E-mailová adresa ověřena na: tu-berlin.de
Nils StrodthoffProfessor for eHealth/AI4Health, Oldenburg University, GermanyE-mailová adresa ověřena na: uol.de

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Julian M. Urban

Postdoc, MIT

E-mailová adresa ověřena na: mit.edu

Lattice QCD Machine Learning


Název Seřadit podle citací Seřadit podle roku Seřadit podle názvu	Citace Citace	Rok
Spectral reconstruction with deep neural networks L Kades, JM Pawlowski, A Rothkopf, M Scherzer, JM Urban, SJ Wetzel, ... Physical Review D 102 (9), 096001, 2020	77	2020
Reconstructing QCD spectral functions with Gaussian processes J Horak, JM Pawlowski, J Rodríguez-Quintero, J Turnwald, JM Urban, ... Physical Review D 105 (3), 036014, 2022	74	2022
Reducing autocorrelation times in lattice simulations with generative adversarial networks JM Pawlowski, JM Urban Machine Learning: Science and Technology 1 (4), 045011, 2020	72	2020
Flow-based sampling for fermionic lattice field theories MS Albergo, G Kanwar, S Racanière, DJ Rezende, JM Urban, D Boyda, ... Physical Review D 104 (11), 114507, 2021	59	2021
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions R Abbott, MS Albergo, D Boyda, K Cranmer, DC Hackett, G Kanwar, ... Physical Review D 106 (7), 074506, 2022	54	2022
Towards novel insights in lattice field theory with explainable machine learning S Blücher, L Kades, JM Pawlowski, N Strodthoff, JM Urban Physical Review D 101 (9), 094507, 2020	54	2020
Flow-based sampling in the lattice Schwinger model at criticality MS Albergo, D Boyda, K Cranmer, DC Hackett, G Kanwar, S Racanière, ... Physical Review D 106 (1), 014514, 2022	40	2022
Aspects of scaling and scalability for flow-based sampling of lattice QCD R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ... The European Physical Journal A 59 (11), 257, 2023	31	2023
Sampling QCD field configurations with gauge-equivariant flow models R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ... arXiv preprint arXiv:2208.03832, 2022	22	2022
Yang-Mills glueball masses from spectral reconstruction JM Pawlowski, CS Schneider, J Turnwald, JM Urban, N Wink Physical Review D 108 (7), 076018, 2023	21	2023
Normalizing flows for lattice gauge theory in arbitrary space-time dimension R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ... arXiv preprint arXiv:2305.02402, 2023	21	2023
Nonperturbative strong coupling at timelike momenta J Horak, JM Pawlowski, J Turnwald, JM Urban, N Wink, S Zafeiropoulos Physical Review D 107 (7), 076019, 2023	20	2023
Confinement in non-Abelian lattice gauge theory via persistent homology D Spitz, JM Urban, JM Pawlowski Physical Review D 107 (3), 034506, 2023	12	2023
Applications of flow models to the generation of correlated lattice QCD ensembles R Abbott, A Botev, D Boyda, DC Hackett, G Kanwar, S Racanière, ... Physical Review D 109 (9), 094514, 2024	11	2024
Flow-based density of states for complex actions JM Pawlowski, JM Urban Physical Review D 108 (5), 054511, 2023	11	2023
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions, 2022 R Abbott, MS Albergo, D Boyda, K Cranmer, DC Hackett, G Kanwar, ... URL https://arxiv. org/abs/2207.08945, 0	5
Practical applications of machine-learned flows on gauge fields R Abbott, MS Albergo, D Boyda, DC Hackett, G Kanwar, F Romero-López, ... arXiv preprint arXiv:2404.11674, 2024	3	2024
Multiscale Normalizing Flows for Gauge Theories R Abbott, MS Albergo, D Boyda, DC Hackett, G Kanwar, F Romero-López, ... arXiv preprint arXiv:2404.10819, 2024	3	2024
Flow-based density of states for complex actions (2022) JM Pawlowski, JM Urban arXiv preprint arXiv:2203.01243, 0	3
Progress in Normalizing Flows for 4d Gauge Theories R Abbott, D Boyda, DC Hackett, G Kanwar, F Romero-López, ... arXiv preprint arXiv:2502.00263, 2025		2025

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