Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Dimitris G. Giovanis"'
Autor:
Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukas Novak, Promit Chakroborty, Himanshu Sharma, Mohit Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner
Publikováno v:
SoftwareX, Vol 24, Iss , Pp 101561- (2023)
This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to sim
Externí odkaz:
https://doaj.org/article/a3229a45b73149a4a3580b2909b45778
Autor:
Kshitiz Upadhyay, Dimitris G. Giovanis, Ahmed Alshareef, Andrew K. Knutsen, Curtis L. Johnson, Aaron Carass, Philip V. Bayly, Michael D. Shields, K.T. Ramesh
Computational models of the human head are promising tools for estimating the impact-induced response of brain, and thus play an important role in the prediction of traumatic brain injury. Modern biofidelic head model simulations are associated with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b86dcbb7bc48033bbd1e9ead8acbd6bb
http://arxiv.org/abs/2110.15553
http://arxiv.org/abs/2110.15553
Publikováno v:
Probabilistic Engineering Mechanics. 55:90-101
The present paper proposes a stochastic formulation which enables the effective coupling of spectral stochastic finite elements with geometrically nonlinear analysis of framed structures. This is achieved by projecting the stochastic part of the incr
Publikováno v:
International Journal for Numerical Methods in Engineering. 117:1079-1116
This paper introduces a surrogate modeling scheme based on Grassmannian manifold learning to be used for cost-efficient predictions of high-dimensional stochastic systems. The method exploits subspace-structured features of each solution by projectin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e74642dcf896080c871e4bf6abac4c2a
http://arxiv.org/abs/2003.11910
http://arxiv.org/abs/2003.11910
Publikováno v:
Journal of Computational Physics. 364:393-415
This paper addresses uncertainty quantification (UQ) for problems where scalar (or low-dimensional vector) response quantities are insufficient and, instead, full-field (very high-dimensional) responses are of interest. To do so, an adaptive stochast
Publikováno v:
Computer Methods in Applied Mechanics and Engineering. 328:411-430
This paper presents a neural network (NN)-based surrogate modeling approach suitable for the geometrically nonlinear analysis of carbon nanotubes (CNTs). In this work we propose an NN-based equivalent beam element (NN-EBE) which is capable of accurat
Publikováno v:
Computer Methods in Applied Mechanics and Engineering. 327:392-410
In intrusive methods for the stochastic analysis of engineering systems, the solution of the stochastic partial differential equations leads to an augmented algebraic system of equations, with respect to the corresponding deterministic problem. The s
Publikováno v:
Computer Methods in Applied Mechanics and Engineering. 319:124-145
We propose a hybrid methodology that implements artificial neural networks (ANN) in the framework of Bayesian updating with structural reliability methods (BUS) in order to increase the computational efficiency of BUS in sampling-based Bayesian infer
Autor:
Fabrice Detrez, Xiaoxin Lu, Julien Yvonnet, Dimitris G. Giovanis, Vissarion Papadopoulos, Jinbo Bai
Publikováno v:
Computational Mechanics
Computational Mechanics, Springer Verlag, 2019, 64 (2), pp.307-321. ⟨10.1007/s00466-018-1643-0⟩
Computational Mechanics, Springer Verlag, 2019, 64 (2), pp.307-321. ⟨10.1007/s00466-018-1643-0⟩
In this paper, a data-driven-based computational homogenization method based on neural networks is proposed to describe the nonlinear electric conduction in random graphene-polymer nanocomposites. In the proposed technique, the nonlinear effective el
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc505344c98f2c049e106f791ce1b0d0
https://hal.archives-ouvertes.fr/hal-02265344
https://hal.archives-ouvertes.fr/hal-02265344