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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
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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

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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
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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

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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications

Samaniego, E. ; Anitescu, C. ; Goswami, S. ; Nguyen-Thanh, V.M. ; Guo, H. ; Hamdia, K. ; Zhuang, X. ; Rabczuk, T.

Computer methods in applied mechanics and engineering, 2020-04, Vol.362, p.112790, Article 112790 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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4
Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions
Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions
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Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

Teichert, G.H. ; Natarajan, A.R. ; Van der Ven, A. ; Garikipati, K.

Computer methods in applied mechanics and engineering, 2019-08, Vol.353, p.201-216 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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5
The stochastic finite element method: Past, present and future
The stochastic finite element method: Past, present and future
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The stochastic finite element method: Past, present and future

Stefanou, George

Computer methods in applied mechanics and engineering, 2009-02, Vol.198 (9), p.1031-1051 [Periódico revisado por pares]

Kidlington: Elsevier B.V

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A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing
A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing
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A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing

Roy, Christopher J. ; Oberkampf, William L.

Computer methods in applied mechanics and engineering, 2011-06, Vol.200 (25), p.2131-2144 [Periódico revisado por pares]

Kidlington: Elsevier B.V

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A priori model reduction through Proper Generalized Decomposition for solving time-dependent partial differential equations
A priori model reduction through Proper Generalized Decomposition for solving time-dependent partial differential equations
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A priori model reduction through Proper Generalized Decomposition for solving time-dependent partial differential equations

Nouy, Anthony

Computer methods in applied mechanics and engineering, 2010-04, Vol.199 (23), p.1603-1626 [Periódico revisado por pares]

Kidlington: Elsevier B.V

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8
A three-dimensional large deformation meshfree method for arbitrary evolving cracks
A three-dimensional large deformation meshfree method for arbitrary evolving cracks
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A three-dimensional large deformation meshfree method for arbitrary evolving cracks

Rabczuk, T. ; Belytschko, T.

Computer methods in applied mechanics and engineering, 2007-05, Vol.196 (29), p.2777-2799 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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An overview of projection methods for incompressible flows
An overview of projection methods for incompressible flows
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An overview of projection methods for incompressible flows

Guermond, J.L. ; Minev, P. ; Shen, Jie

Computer methods in applied mechanics and engineering, 2006-09, Vol.195 (44), p.6011-6045 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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A topology optimization method based on the level set method incorporating a fictitious interface energy
A topology optimization method based on the level set method incorporating a fictitious interface energy
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A topology optimization method based on the level set method incorporating a fictitious interface energy

Yamada, Takayuki ; Izui, Kazuhiro ; Nishiwaki, Shinji ; Takezawa, Akihiro

Computer methods in applied mechanics and engineering, 2010-11, Vol.199 (45), p.2876-2891 [Periódico revisado por pares]

Kidlington: Elsevier B.V

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