Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Guido Novati"'
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-7 (2021)
Navigation and trajectory planning in environments with background flow, relevant for robotics, are challenging provided information only on local surrounding. The authors propose a reinforcement learning approach for time-efficient navigation of a s
Externí odkaz:
https://doaj.org/article/238d3bca93c848cb91cb01cd92dc5160
Autor:
Pascal Weber, Georgios Arampatzis, Guido Novati, Siddhartha Verma, Costas Papadimitriou, Petros Koumoutsakos
Publikováno v:
Biomimetics, Vol 5, Iss 1, p 10 (2020)
Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quan
Externí odkaz:
https://doaj.org/article/c932bd81890f4d21ac6ab39a600d310d
Autor:
Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, Mark S. Graham, Pedro Borges, Virginia Fernandez, Dai Yang, Jeremy Appleyard, Guido Novati, Disha Mehra, Mike Vella, Parashkev Nachev, Sebastien Ourselin, Jorge Cardoso
Publikováno v:
Simulation and Synthesis in Medical Imaging ISBN: 9783031169793
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a517e19035dad4461ae0db7835f6049f
https://doi.org/10.1007/978-3-031-16980-9_7
https://doi.org/10.1007/978-3-031-16980-9_7
Publikováno v:
Nature Machine Intelligence, 3 (1)
Turbulent flow models are critical for applications such as aircraft design, weather forecasting and climate prediction. Existing models are largely based on physical insight and engineering intuition. More recently, machine learning has been contrib
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2aa3b5668cafcf1e866c44324f921557
https://hdl.handle.net/20.500.11850/461724
https://hdl.handle.net/20.500.11850/461724
Publikováno v:
International Journal for Numerical Methods in Fluids. 85:484-501
The distribution of forces on the surface of complex, deforming geometries is an invaluable output of flow simulations. One particular example of such geometries involves self-propelled swimmers. Surface forces can provide significant information abo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2f6efc08ba036a3bb130cb8eb31a73d
https://doi.org/10.1002/fld.4392
https://doi.org/10.1002/fld.4392
Publikováno v:
ICCS
Heaving airfoils can provide invaluable physical insight regarding the flapping flight of birds and insects. We examine the thrust-generation mechanism of oscillating foils, by coupling two-dimensional simulations with multi-objective optimization al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::feec07e54db99eaf66b4e9152ea02a86
https://doi.org/10.1016/j.procs.2017.05.166
https://doi.org/10.1016/j.procs.2017.05.166
Autor:
Georgios Arampatzis, Petros Koumoutsakos, Siddhartha Verma, Pascal Weber, Costas Papadimitriou, Guido Novati
Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0e18571800644c03a36bc63cd343410
Publikováno v:
Physical Review Fluids. 4
Gliding is an energetically efficient mode of transportation. It is shown that reinforcement learning identifies gliding strategies with minimum energy expenditure and fastest time of arrival, with better performance than model-based optimal control,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97eefd8a72187a27df4e829a8521cdbe
https://doi.org/10.1103/physrevfluids.4.093902
https://doi.org/10.1103/physrevfluids.4.093902
Publikováno v:
Nature Machine Intelligence. 3:99-99
A Correction to this paper has been published: http://doi-org-443.webvpn.bjmu.tsg211.com/10.1038/s42256-021-00295-1 .
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::950718bc88c327963cc87f1face72abd
https://doi.org/10.1038/s42256-021-00295-1
https://doi.org/10.1038/s42256-021-00295-1
Publikováno v:
Nature Machine Intelligence. 3:98-98
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b316347c4a0ee5a33db845b1d9f1ee7b
https://doi.org/10.1038/s42256-021-00293-3
https://doi.org/10.1038/s42256-021-00293-3