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On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
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On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

Wang, Sifan ; Wang, Hanwen ; Perdikaris, Paris

Computer methods in applied mechanics and engineering, 2021-10, Vol.384 (C), p.113938, Article 113938 [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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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
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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

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On the BFGS monolithic algorithm for the unified phase field damage theory
On the BFGS monolithic algorithm for the unified phase field damage theory
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On the BFGS monolithic algorithm for the unified phase field damage theory

Wu, Jian-Ying ; Huang, Yuli ; Nguyen, Vinh Phu

Computer methods in applied mechanics and engineering, 2020-03, Vol.360, p.112704, Article 112704 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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A phase-field formulation for dynamic cohesive fracture
A phase-field formulation for dynamic cohesive fracture
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A phase-field formulation for dynamic cohesive fracture

Geelen, Rudy J.M. ; Liu, Yingjie ; Hu, Tianchen ; Tupek, Michael R. ; Dolbow, John E.

Computer methods in applied mechanics and engineering, 2019-05, Vol.348 (C), p.680-711 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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Phase-field analysis of finite-strain plates and shells including element subdivision
Phase-field analysis of finite-strain plates and shells including element subdivision
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Phase-field analysis of finite-strain plates and shells including element subdivision

Areias, P. ; Rabczuk, T. ; Msekh, M.A.

Computer methods in applied mechanics and engineering, 2016-12, Vol.312, p.322-350 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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7
Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
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Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening

Vlassis, Nikolaos N. ; Sun, WaiChing

Computer methods in applied mechanics and engineering, 2021-04, Vol.377, p.113695, Article 113695 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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Minimum compliance topology optimization of shell–infill composites for additive manufacturing
Minimum compliance topology optimization of shell–infill composites for additive manufacturing
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Minimum compliance topology optimization of shell–infill composites for additive manufacturing

Wu, Jun ; Clausen, Anders ; Sigmund, Ole

Computer methods in applied mechanics and engineering, 2017-11, Vol.326, p.358-375 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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A line search assisted monolithic approach for phase-field computing of brittle fracture
A line search assisted monolithic approach for phase-field computing of brittle fracture
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A line search assisted monolithic approach for phase-field computing of brittle fracture

Gerasimov, T. ; De Lorenzis, L.

Computer methods in applied mechanics and engineering, 2016-12, Vol.312, p.276-303 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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10
Non-invasive inference of thrombus material properties with physics-informed neural networks
Non-invasive inference of thrombus material properties with physics-informed neural networks
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Non-invasive inference of thrombus material properties with physics-informed neural networks

Yin, Minglang ; Zheng, Xiaoning ; Humphrey, Jay D. ; Karniadakis, George Em

Computer methods in applied mechanics and engineering, 2021-03, Vol.375, p.113603, Article 113603 [Periódico revisado por pares]

Netherlands: Elsevier B.V

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