Mapping distinct phase transitions to a neural network D Bachtis, G Aarts, B Lucini Physical Review E 102 (5), 053306, 2020 | 34 | 2020 |
Extending machine learning classification capabilities with histogram reweighting D Bachtis, G Aarts, B Lucini Physical Review E 102 (3), 033303, 2020 | 31 | 2020 |
Quantum field-theoretic machine learning D Bachtis, G Aarts, B Lucini Physical Review D 103 (7), 074510, 2021 | 29 | 2021 |
Inverse renormalization group in quantum field theory D Bachtis, G Aarts, F Di Renzo, B Lucini Physical Review Letters 128 (8), 081603, 2022 | 21 | 2022 |
Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration D Bachtis, G Aarts, B Lucini Physical Review Research 3 (1), 013134, 2021 | 19 | 2021 |
Phase transitions in particle physics: Results and perspectives from lattice Quantum Chromo-Dynamics G Aarts, J Aichelin, C Allton, A Athenodorou, D Bachtis, C Bonanno, ... Progress in Particle and Nuclear Physics, 104070, 2023 | 11 | 2023 |
Quantum field theories, Markov random fields and machine learning D Bachtis, G Aarts, B Lucini Journal of Physics: Conference Series 2207 (1), 012056, 2022 | 4 | 2022 |
Interpreting machine learning functions as physical observables G Aarts, D Bachtis, B Lucini arXiv preprint arXiv:2109.08497, 2021 | 1 | 2021 |
Machine learning with quantum field theories D Bachtis, G Aarts, B Lucini arXiv preprint arXiv:2109.07730, 2021 | 1 | 2021 |
Overlap renormalization group transformations for disordered systems D Bachtis arXiv preprint arXiv:2302.08459, 2023 | | 2023 |
Reducing finite-size effects in quantum field theories with the renormalization group D Bachtis arXiv preprint arXiv:2205.08156, 2022 | | 2022 |
Quantum field-theoretic machine learning and the renormalization group DS Bachtis Swansea University, 2022 | | 2022 |
Una conexión inesperada entre la física de partículas y la inteligencia artificial D Bachtis Investigación y ciencia, 2021 | | 2021 |