Zobrazeno 1 - 10
of 318
pro vyhledávání: '"Canuto, Claudio"'
This is a survey on the theory of adaptive finite element methods (AFEMs), which are fundamental in modern computational science and engineering but whose mathematical assessment is a formidable challenge. We present a self-contained and up-to-date d
Externí odkaz:
http://arxiv.org/abs/2402.07273
Autor:
Yang, Cheng-Hau, Saurabh, Kumar, Scovazzi, Guglielmo, Canuto, Claudio, Krishnamurthy, Adarsh, Ganapathysubramanian, Baskar
The accurate and efficient simulation of Partial Differential Equations (PDEs) in and around arbitrarily defined geometries is critical for many application domains. Immersed boundary methods (IBMs) alleviate the usually laborious and time-consuming
Externí odkaz:
http://arxiv.org/abs/2307.01479
We propose, analyze, and investigate numerically a novel feedback control strategy for high Reynolds number flows. For both the continuous and the discrete (finite element) settings, we prove that the new strategy yields accurate results for high Rey
Externí odkaz:
http://arxiv.org/abs/2307.00675
Autor:
Canuto, Claudio, Fassino, Davide
Publikováno v:
Mathematics in Engineering, 5(6), 1-33. 2023
The realization of a standard Adaptive Finite Element Method (AFEM) preserves the mesh conformity by performing a completion step in the refinement loop: in addition to elements marked for refinement due to their contribution to the global error esti
Externí odkaz:
http://arxiv.org/abs/2306.07064
In this manuscript we propose and analyze weighted reduced order methods for stochastic Stokes and Navier-Stokes problems depending on random input data (such as forcing terms, physical or geometrical coefficients, boundary conditions). We will compa
Externí odkaz:
http://arxiv.org/abs/2303.14432
Autor:
Canuto, Claudio, Rosso, Davide
We consider the Virtual Element method (VEM) introduced by Beir\~ao da Veiga, Lovadina and Vacca in 2016 for the numerical solution of the steady, incompressible Navier-Stokes equations; the method has arbitrary order $k \geq 2$ and guarantees diverg
Externí odkaz:
http://arxiv.org/abs/2212.14414
Publikováno v:
Ann Univ Ferrara 68, 575-595 (2022)
We consider the discretization of elliptic boundary-value problems by variational physics-informed neural networks (VPINNs), in which test functions are continuous, piecewise linear functions on a triangulation of the domain. We define an a posterior
Externí odkaz:
http://arxiv.org/abs/2205.00786
Publikováno v:
J. Sci. Comput. 92, 100 (2022)
In this work we analyze how quadrature rules of different precisions and piecewise polynomial test functions of different degrees affect the convergence rate of Variational Physics Informed Neural Networks (VPINN) with respect to mesh refinement, whi
Externí odkaz:
http://arxiv.org/abs/2109.02035
Autor:
Zorrilla, Rubén, Rossi, Riccardo, Scovazzi, Guglielmo, Canuto, Claudio, Rodríguez-Ferran, Antonio
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 March 2024 421
Autor:
Yang, Cheng-Hau, Saurabh, Kumar, Scovazzi, Guglielmo, Canuto, Claudio, Krishnamurthy, Adarsh, Ganapathysubramanian, Baskar
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 February 2024 419