Phiala E. Shanahan

23 papers A* 1Journal 21Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Variance reduction in lattice QCD observables via normalizing flows. CoRR Ryan Abbott, Denis Boyda, Yang Fu, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
2025	J	`jnl`	Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge Theory. CoRR Yixuan Sun, Srinivas Eswar, Yin Lin, William Detmold, Phiala E. Shanahan, Xiaoye Li, Yang Liu, Prasanna Balaprakash
2024	J	`jnl`	Applications of flow models to the generation of correlated lattice QCD ensembles. CoRR 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
2024	J	`jnl`	Exploring gauge-fixing conditions with gradient-based optimization. CoRR William Detmold, Gurtej Kanwar, Yin Lin, Phiala E. Shanahan, Michael L. Wagman
2024	J	`jnl`	Practical applications of machine-learned flows on gauge fields. CoRR Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
2023	J	`jnl`	Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics. CoRR Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan
2023	J	`jnl`	Normalizing flows for lattice gauge theory in arbitrary space-time dimension. CoRR 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
2022	J	`jnl`	Applications of Machine Learning to Lattice Quantum Field Theory. CoRR Denis Boyda, Salvatore Calì, Sam Foreman, Lena Funcke, Daniel C. Hackett, Yin Lin, Gert Aarts, Andrei Alexandru, Xiao-Yong Jin, Biagio Lucini, Phiala E. Shanahan
2022	J	`jnl`	Aspects of scaling and scalability for flow-based sampling of lattice QCD. CoRR 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
2022	J	`jnl`	Flow-based sampling in the lattice Schwinger model at criticality. CoRR 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
2022	J	`jnl`	Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions. CoRR 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
2022	J	`jnl`	Neural-network preconditioners for solving the Dirac equation in lattice gauge theory. CoRR Salvatore Calì, Daniel C. Hackett, Yin Lin, Phiala E. Shanahan, Brian Xiao
2022	J	`jnl`	Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning. CoRR Phiala E. Shanahan, Kazuhiro Terao, Daniel Whiteson
2022	J	`jnl`	Symmetry Group Equivariant Architectures for Physics. CoRR Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf, Risi Kondor, David W. Miller, Daniel Murnane, Jan T. Offermann, Mariel Pettee, Phiala E. Shanahan, Chase Shimmin, Savannah Thais
2021	J	`jnl`	Flow-based sampling for fermionic lattice field theories. CoRR Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
2021	J	`jnl`	Flow-based sampling for multimodal distributions in lattice field theory. CoRR Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan
2021	J	`jnl`	Introduction to Normalizing Flows for Lattice Field Theory. CoRR Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
2020	J	`jnl`	Equivariant flow-based sampling for lattice gauge theory. CoRR Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
2020	A*	`conf`	Normalizing Flows on Tori and Spheres. ICML Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer
2020	J	`jnl`	Normalizing Flows on Tori and Spheres. CoRR Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer
2020	J	`jnl`	Sampling using SU(N) gauge equivariant flows. CoRR Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
2019	J	`jnl`	Flow-based generative models for Markov chain Monte Carlo in lattice field theory. CoRR Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan
2018	—	`conf`	Neural network parameter regression for lattice quantum chromodynamics simulations in nuclear and particle physics. ICLR (Workshop) Phiala E. Shanahan, Daniel Trewartha, William Detmold