A cluster algorithm for lattice gauge theories. - Fabien Alet; Biagio Lucini; Michele Vettorazzo (Comput. Phys. Commun., 2005)

BSMBench: a flexible and scalable supercomputer benchmark from computational particle physics. - Ed Bennett; Luigi Del Debbio; Kirk E. Jordan; Biagio Lucini; Agostino Patella; Claudio Pica; Antonio Rago 002 (CoRR, 2014)

BSMBench: A flexible and scalable HPC benchmark from beyond the standard model physics. - Ed Bennett; Biagio Lucini; Luigi Del Debbio; Kirk E. Jordan; Agostino Patella; Claudio Pica; Antonio Rago 002 (HPCS, 2016)

Extending machine learning classification capabilities with histogram reweighting. - Dimitrios Bachtis; Gert Aarts; Biagio Lucini (CoRR, 2020)

Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration. - Dimitrios Bachtis; Gert Aarts; Biagio Lucini (CoRR, 2020)

Quantum field-theoretic machine learning. - Dimitrios Bachtis; Gert Aarts; Biagio Lucini (CoRR, 2021)

Machine learning with quantum field theories. - Dimitrios Bachtis; Gert Aarts; Biagio Lucini (CoRR, 2021)

Quantitative analysis of phase transitions in two-dimensional XY models using persistent homology. - Nicholas Sale; Jeffrey Giansiracusa; Biagio Lucini (CoRR, 2021)

Quantum field theories, Markov random fields and machine learning. - Dimitrios Bachtis; Gert Aarts; Biagio Lucini (CoRR, 2021)

Applications of Machine Learning to Lattice Quantum Field Theory. - 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 (CoRR, 2022)

Stochastic weight matrix dynamics during learning and Dyson Brownian motion. - Gert Aarts; Biagio Lucini; Chanju Park (CoRR, 2024)

Dyson Brownian motion and random matrix dynamics of weight matrices during learning. - Gert Aarts; Ouraman Hajizadeh; Biagio Lucini; Chanju Park (CoRR, 2024)

Random Matrix Theory for Stochastic Gradient Descent. - Chanju Park; Matteo Favoni; Biagio Lucini; Gert Aarts (CoRR, 2024)