skip to main content
Idioma:
Resultados 1 2 3 4 5 next page
Refinado por: Nome da Publicação: Computer Methods In Applied Mechanics And Engineering remover assunto: Engineering remover
Result Number Material Type Add to My Shelf Action Record Details and Options
1
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
Material Type:
Artigo 		Adicionar ao Meu Espaço

A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

Haghighat, Ehsan ; Raissi, Maziar ; Moure, Adrian ; Gomez, Hector ; Juanes, Ruben

Computer methods in applied mechanics and engineering, 2021-06, Vol.379, p.113741, Article 113741 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

Texto completo disponível

Citações Citado por
2
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Material Type:
Artigo 		Adicionar ao Meu Espaço

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

Sun, Luning ; Gao, Han ; Pan, Shaowu ; Wang, Jian-Xun

Computer methods in applied mechanics and engineering, 2020-04, Vol.361, p.112732, Article 112732 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

Texto completo disponível

Citações Citado por
3
Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
Material Type:
Artigo 		Adicionar ao Meu Espaço

Physics-informed multi-LSTM networks for metamodeling of nonlinear structures

Zhang, Ruiyang ; Liu, Yang ; Sun, Hao

Computer methods in applied mechanics and engineering, 2020-09, Vol.369, p.113226, Article 113226 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

Texto completo disponível

Citações Citado por
4
A physics-informed operator regression framework for extracting data-driven continuum models
A physics-informed operator regression framework for extracting data-driven continuum models
Material Type:
Artigo 		Adicionar ao Meu Espaço

A physics-informed operator regression framework for extracting data-driven continuum models

Patel, Ravi G. ; Trask, Nathaniel A. ; Wood, Mitchell A. ; Cyr, Eric C.

Computer methods in applied mechanics and engineering, 2021-01, Vol.373 (C), p.113500, Article 113500 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

Texto completo disponível

Citações Citado por
5
CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method
CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method
Material Type:
Artigo 		Adicionar ao Meu Espaço

CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method

Chiu, Pao-Hsiung ; Wong, Jian Cheng ; Ooi, Chinchun ; Dao, My Ha ; Ong, Yew-Soon

Computer methods in applied mechanics and engineering, 2022-05, Vol.395, p.114909, Article 114909 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

Texto completo disponível

Citações Citado por
6
Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
Material Type:
Artigo 		Adicionar ao Meu Espaço

Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture

Amini Niaki, Sina ; Haghighat, Ehsan ; Campbell, Trevor ; Poursartip, Anoush ; Vaziri, Reza

Computer methods in applied mechanics and engineering, 2021-10, Vol.384, p.113959, Article 113959 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

Texto completo disponível

Citações Citado por
7
PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
Material Type:
Artigo 		Adicionar ao Meu Espaço

PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs

Ren, Pu ; Rao, Chengping ; Liu, Yang ; Wang, Jian-Xun ; Sun, Hao

Computer methods in applied mechanics and engineering, 2022-02, Vol.389, p.114399, Article 114399 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

Texto completo disponível

Citações Citado por
8
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
Material Type:
Artigo 		Adicionar ao Meu Espaço

A nonlocal physics-informed deep learning framework using the peridynamic differential operator

Haghighat, Ehsan ; Bekar, Ali Can ; Madenci, Erdogan ; Juanes, Ruben

Computer methods in applied mechanics and engineering, 2021-11, Vol.385, p.114012, Article 114012 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

Texto completo disponível

Citações Citado por
9
A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations
A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations
Material Type:
Artigo 		Adicionar ao Meu Espaço

A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations

Mattey, Revanth ; Ghosh, Susanta

Computer methods in applied mechanics and engineering, 2022-02, Vol.390, p.114474, Article 114474 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

Texto completo disponível

Citações Citado por
10
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
Material Type:
Artigo 		Adicionar ao Meu Espaço

On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling

Fuhg, Jan N. ; Bouklas, Nikolaos

Computer methods in applied mechanics and engineering, 2022-05, Vol.394, p.114915, Article 114915 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

Texto completo disponível

Citações Citado por
Resultados 1 2 3 4 5 next page

Personalize Seus Resultados

  1. Editar

Refine Search Results

Expandir Meus Resultados

  1.   

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.