Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Patricio Clark Di Leoni"'
Publikováno v:
Experiments in Fluids. 64
Synthetic dataset used in case 2 of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::809717e38d242c3f7839a7bc5c002a5b
Publikováno v:
The European Physical Journal E. 46
We investigate the capabilities of Physics-Informed Neural Networks (PINNs) to reconstruct turbulent Rayleigh-Benard flows using only temperature information. We perform a quantitative analysis of the quality of the reconstructions at various amounts
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57e500ecee4b328fd566a6c5e4949bd4
http://arxiv.org/abs/2301.07769
http://arxiv.org/abs/2301.07769
Publikováno v:
Physical Review X, Vol 10, Iss 1, p 011023 (2020)
Nudging is an important data assimilation technique where partial field measurements are used to control the evolution of a dynamical system and/or to reconstruct the entire phase-space configuration of the supplied flow. Here, we apply it to the can
Externí odkaz:
https://doaj.org/article/5dad0aff87d04120a6b1ade068998cc3
Publikováno v:
Journal of Computational Physics. 474:111793
Publikováno v:
Journal of Fluid Mechanics. 914
By analysing the Karman–Howarth equation for filtered-velocity fields in turbulent flows, we show that the two-point correlation between the filtered strain-rate and subfilter stress tensors plays a central role in the evolution of filtered-velocit
Autor:
Michele Buzzicotti, Kristian Gustavsson, Luca Biferale, Fabio Bonaccorso, Patricio Clark Di Leoni
Publikováno v:
AIxIA 2020 – Advances in Artificial Intelligence ISBN: 9783030770907
AI*IA
AI*IA
We present theoretical and numerical results concerning the problem to find the path that minimizes the time to navigate between two given points in a complex fluid under realistic navigation constraints. We contrast deterministic Optimal Navigation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf52e7b8081dfa9035669335a9b62fcf
http://hdl.handle.net/2108/289859
http://hdl.handle.net/2108/289859
Autor:
George Em Karniadakis, Khemraj Shukla, James L. Blackshire, Daniel Sparkman, Patricio Clark Di Leoni
We introduce an optimized physics-informed neural network (PINN) trained to solve the problem of identifying and characterizing a surface breaking crack in a metal plate. PINNs are neural networks that can combine data and physics in the learning pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d476ce9ce9bbec253046b0ff8dd69a53
http://arxiv.org/abs/2005.03596
http://arxiv.org/abs/2005.03596
Publikováno v:
Physical Review X, Vol 10, Iss 1, p 011023 (2020)
Nudging is an important data assimilation technique where partial field measurements are used to control the evolution of a dynamical system and/or to reconstruct the entire phase-space configuration of the supplied flow. Here, we apply it to the tou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85b6bdc63f9dcff8db1a048ee83eb944
https://hdl.handle.net/11567/1012928
https://hdl.handle.net/11567/1012928