Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Arthur N Montanari

Showing results (1-10 of 10) with videos related to

Pageof 1
Sort By:
Chaos (Woodbury, N.Y.)|April 1, 2019
Particle filtering of dynamical networks: Highlighting observability issuesArthur N Montanari, Luis A Aguirre
Physical Review. E|October 21, 2022
Buffering variability in cell regulation motifs close to criticalityDaniele Proverbio, Arthur N Montanari, Alexander Skupin, et al.
Chaos (Woodbury, N.Y.)|April 1, 2023
Power-grid vulnerability and its relation with network structureJussara Dias, Arthur N Montanari, Elbert E N Macau
Physical Review Letters|November 21, 2025
Interpretable Disorder-Promoted Synchronization and Coherence in Coupled Laser NetworksAna Elisa D Barioni, Arthur N Montanari, Adilson E Motter
Proceedings of the National Academy of Sciences of the United States of America|December 31, 2021
Functional observability and target state estimation in large-scale networksArthur N Montanari, Chao Duan, Luis A Aguirre, et al.
Nature Communications|November 1, 2025
Optimal flock formation induced by agent heterogeneityArthur N Montanari, Ana Elisa D Barioni, Chao Duan, et al.
NPJ Systems Biology and Applications|October 14, 2025
Multi-omic network inference from time-series dataMaría Moscardó García, Atte Aalto, Arthur N Montanari, et al.
Chaos (Woodbury, N.Y.)|September 2, 2019
The reliability of recurrence network analysis is influenced by the observability properties of the recorded time seriesLeonardo L Portes, Arthur N Montanari, Debora C Correa, et al.
NPJ Digital Medicine|June 1, 2024
Towards automatic home-based sleep apnea estimation using deep learningGabriela Retamales, Marino E Gavidia, Ben Bausch, et al.
Patterns (New York, N.Y.)|July 15, 2024
Early warning of atrial fibrillation using deep learningMarino Gavidia, Hongling Zhu, Arthur N Montanari, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Chaos (Woodbury, N.Y.)|April 1, 2019
Particle filtering of dynamical networks: Highlighting observability issuesArthur N Montanari, Luis A Aguirre
Physical Review. E|October 21, 2022
Buffering variability in cell regulation motifs close to criticalityDaniele Proverbio, Arthur N Montanari, Alexander Skupin, et al.
Chaos (Woodbury, N.Y.)|April 1, 2023
Power-grid vulnerability and its relation with network structureJussara Dias, Arthur N Montanari, Elbert E N Macau
Physical Review Letters|November 21, 2025
Interpretable Disorder-Promoted Synchronization and Coherence in Coupled Laser NetworksAna Elisa D Barioni, Arthur N Montanari, Adilson E Motter
Proceedings of the National Academy of Sciences of the United States of America|December 31, 2021
Functional observability and target state estimation in large-scale networksArthur N Montanari, Chao Duan, Luis A Aguirre, et al.
Nature Communications|November 1, 2025
Optimal flock formation induced by agent heterogeneityArthur N Montanari, Ana Elisa D Barioni, Chao Duan, et al.
NPJ Systems Biology and Applications|October 14, 2025
Multi-omic network inference from time-series dataMaría Moscardó García, Atte Aalto, Arthur N Montanari, et al.
Chaos (Woodbury, N.Y.)|September 2, 2019
The reliability of recurrence network analysis is influenced by the observability properties of the recorded time seriesLeonardo L Portes, Arthur N Montanari, Debora C Correa, et al.
NPJ Digital Medicine|June 1, 2024
Towards automatic home-based sleep apnea estimation using deep learningGabriela Retamales, Marino E Gavidia, Ben Bausch, et al.
Patterns (New York, N.Y.)|July 15, 2024
Early warning of atrial fibrillation using deep learningMarino Gavidia, Hongling Zhu, Arthur N Montanari, et al.
Pageof 1