Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Francesco Borra"'
Autor:
Fernando Domingues Amaro, Rita Antonietti, Elisabetta Baracchini, Luigi Benussi, Stefano Bianco, Francesco Borra, Cesidio Capoccia, Michele Caponero, Danilo Santos Cardoso, Gianluca Cavoto, Igor Abritta Costa, Emiliano Dané, Giorgio Dho, Flaminia Di Giambattista, Emanuele Di Marco, Giulia D’Imperio, Joaquim Marques Ferreira dos Santos, Giovanni Grilli di Cortona, Francesco Iacoangeli, Ernesto Kemp, Herman Pessoa Lima Júnior, Guilherme Sebastiao Pinheiro Lopes, Amaro da Silva Lopes Júnior, Giovanni Maccarrone, Rui Daniel Passos Mano, Robert Renz Marcelo Gregorio, David José Gaspar Marques, Giovanni Mazzitelli, Alasdair Gregor McLean, Pietro Meloni, Andrea Messina, Cristina Maria Bernardes Monteiro, Rafael Antunes Nobrega, Igor Fonseca Pains, Emiliano Paoletti, Luciano Passamonti, Sandro Pelosi, Fabrizio Petrucci, Stefano Piacentini, Davide Piccolo, Daniele Pierluigi, Davide Pinci, Atul Prajapati, Francesco Renga, Rita Cruz Roque, Filippo Rosatelli, Alessandro Russo, Giovanna Saviano, Neil John Curwen Spooner, Roberto Tesauro, Sandro Tomassini, Samuele Torelli, Donatella Tozzi
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 83, Iss 10, Pp 1-23 (2023)
Abstract The nature of dark matter is still unknown and an experimental program to look for dark matter particles in our Galaxy should extend its sensitivity to light particles in the GeV mass range and exploit the directional information of the DM p
Externí odkaz:
https://doaj.org/article/7eb270706d154696b0109ed07eab3e5b
Publikováno v:
Communications Physics, Vol 6, Iss 1, Pp 1-10 (2023)
Abstract Pursuing a drifting target in a turbulent flow is an extremely difficult task whenever the searcher has limited propulsion and maneuvering capabilities. Even in the case when the relative distance between pursuer and target stays below the t
Externí odkaz:
https://doaj.org/article/350576c784cb46969913322f2964bd94
Publikováno v:
The European Physical Journal E. 46
We consider the problem of two active particles in 2D complex flows with the multi-objective goals of minimizing both the dispersion rate and the energy consumption of the pair. We approach the problem by means of Multi Objective Reinforcement Learni
Autor:
Francesco Borra, Marco Baldovin
Publikováno v:
Chaos (Woodbury, N.Y.). 31(2)
Machine learning techniques not only offer efficient tools for modelling dynamical systems from data, but can also be employed as frontline investigative instruments for the underlying physics. Nontrivial information about the original dynamics, whic
Publikováno v:
Journal of statistical mechanics 2021 (2021): 083401-1–083401-25. doi:10.1088/1742-5468/ac12c6
info:cnr-pdr/source/autori:Borra F.; Cencini M.; Celani A./titolo:Optimal collision avoidance in swarms of active Brownian particles/doi:10.1088%2F1742-5468%2Fac12c6/rivista:Journal of statistical mechanics/anno:2021/pagina_da:083401-1/pagina_a:083401-25/intervallo_pagine:083401-1–083401-25/volume:2021
info:cnr-pdr/source/autori:Borra F.; Cencini M.; Celani A./titolo:Optimal collision avoidance in swarms of active Brownian particles/doi:10.1088%2F1742-5468%2Fac12c6/rivista:Journal of statistical mechanics/anno:2021/pagina_da:083401-1/pagina_a:083401-25/intervallo_pagine:083401-1–083401-25/volume:2021
The effectiveness of collective navigation of biological or artificial agents requires to accommodate for contrasting requirements, such as staying in a group while avoiding close encounters and at the same time limiting the energy expenditure for ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25da26e3d87ee9681542fc3fdbc93041
https://publications.cnr.it/doc/456322
https://publications.cnr.it/doc/456322
Publikováno v:
Physical review. E (Online) 102 (2020): 052203-1–052203-11. doi:10.1103/PhysRevE.102.052203
info:cnr-pdr/source/autori:Borra F.; Vulpiani A.; Cencini M./titolo:Effective models and predictability of chaotic multiscale systems via machine learning/doi:10.1103%2FPhysRevE.102.052203/rivista:Physical review. E (Online)/anno:2020/pagina_da:052203-1/pagina_a:052203-11/intervallo_pagine:052203-1–052203-11/volume:102
info:cnr-pdr/source/autori:Borra F.; Vulpiani A.; Cencini M./titolo:Effective models and predictability of chaotic multiscale systems via machine learning/doi:10.1103%2FPhysRevE.102.052203/rivista:Physical review. E (Online)/anno:2020/pagina_da:052203-1/pagina_a:052203-11/intervallo_pagine:052203-1–052203-11/volume:102
We scrutinize the use of machine learning, based on reservoir computing, to build data-driven effective models of multiscale chaotic systems. We show that, for a wide scale separation, machine learning generates effective models akin to those obtaine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f12ae97c6f649ce7d036926efa32408
https://journals.aps.org/pre/abstract/10.1103/PhysRevE.102.052203
https://journals.aps.org/pre/abstract/10.1103/PhysRevE.102.052203
Publikováno v:
Journal of Physics A: Mathematical and Theoretical
Generalization is a central aspect of learning theory. Here, we proposea#13; a framework that explores an auxiliary task-dependent notion of gener-a#13; alization, and attempts to quantitatively answer the following question:a#13; given two sets of p
Publikováno v:
Journal of Physics A: Mathematical & Theoretical; 9/20/2019, Vol. 52 Issue 38, p1-1, 1p