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Identification of two-dimensional pantographic structure via a linear D4 orthotropic second gradient elastic model
Identification of two-dimensional pantographic structure via a linear D4 orthotropic second gradient elastic model
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Identification of two-dimensional pantographic structure via a linear D4 orthotropic second gradient elastic model

Placidi, Luca ; Andreaus, Ugo ; Giorgio, Ivan

Journal of engineering mathematics, 2017-04, Vol.103 (1), p.1-21 [Peer Reviewed Journal]

Dordrecht: Springer Netherlands

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2
Effective methods for numerical analysis of the simplest chaotic circuit model with Atangana–Baleanu Caputo fractional derivative
Effective methods for numerical analysis of the simplest chaotic circuit model with Atangana–Baleanu Caputo fractional derivative
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Effective methods for numerical analysis of the simplest chaotic circuit model with Atangana–Baleanu Caputo fractional derivative

Alzahrani, Abdulrahman B. M. ; Saadeh, Rania ; Abdoon, Mohamed A. ; Elbadri, Mohamed ; Berir, Mohammed ; Qazza, Ahmad

Journal of engineering mathematics, 2024-02, Vol.144 (1), Article 9 [Peer Reviewed Journal]

Dordrecht: Springer Netherlands

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3
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 [Peer Reviewed Journal]

Amsterdam: Elsevier B.V

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4
Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
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Deep autoencoders for physics-constrained data-driven nonlinear materials modeling

He, Xiaolong ; He, Qizhi ; Chen, Jiun-Shyan

Computer methods in applied mechanics and engineering, 2021-11, Vol.385 (C), p.114034, Article 114034 [Peer Reviewed Journal]

Amsterdam: Elsevier B.V

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5
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 [Peer Reviewed Journal]

Amsterdam: Elsevier B.V

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6
Generalized squirming motion of a sphere
Generalized squirming motion of a sphere
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Generalized squirming motion of a sphere

Pak, On Shun ; Lauga, Eric

Journal of engineering mathematics, 2014-10, Vol.88 (1), p.1-28 [Peer Reviewed Journal]

Dordrecht: Springer Netherlands

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An enhanced flux continuity three-dimensional finite element method for heterogeneous and anisotropic diffusion problems on general meshes
An enhanced flux continuity three-dimensional finite element method for heterogeneous and anisotropic diffusion problems on general meshes
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An enhanced flux continuity three-dimensional finite element method for heterogeneous and anisotropic diffusion problems on general meshes

Hai, Ong Thanh ; Nguyen, Thi Hoai Thuong ; Le, Anh Ha ; Do, Vuong Nguyen Van

Journal of engineering mathematics, 2024-04, Vol.145 (1), Article 16 [Peer Reviewed Journal]

Dordrecht: Springer Netherlands

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8
Optimal control of higher-order Hilfer fractional non-instantaneous impulsive stochastic integro-differential systems
Optimal control of higher-order Hilfer fractional non-instantaneous impulsive stochastic integro-differential systems
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Optimal control of higher-order Hilfer fractional non-instantaneous impulsive stochastic integro-differential systems

Sathiyaraj, T. ; Balasubramaniam, P. ; Chen, Hao ; Ong, Seng Huat

Journal of engineering mathematics, 2024-06, Vol.146 (1), Article 3 [Peer Reviewed Journal]

Dordrecht: Springer Netherlands

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9
Multiscale Modeling Meets Machine Learning: What Can We Learn?
Multiscale Modeling Meets Machine Learning: What Can We Learn?
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Multiscale Modeling Meets Machine Learning: What Can We Learn?

Peng, Grace C. Y. ; Alber, Mark ; Buganza Tepole, Adrian ; Cannon, William R. ; De, Suvranu ; Dura-Bernal, Savador ; Garikipati, Krishna ; Karniadakis, George ; Lytton, William W. ; Perdikaris, Paris ; Petzold, Linda ; Kuhl, Ellen

Archives of computational methods in engineering, 2021-05, Vol.28 (3), p.1017-1037 [Peer Reviewed Journal]

Dordrecht: Springer Netherlands

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10
On the energy decay of a nonlinear time-fractional Euler–Bernoulli beam problem including time-delay: theoretical treatment and numerical solution techniques
On the energy decay of a nonlinear time-fractional Euler–Bernoulli beam problem including time-delay: theoretical treatment and numerical solution techniques
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On the energy decay of a nonlinear time-fractional Euler–Bernoulli beam problem including time-delay: theoretical treatment and numerical solution techniques

Bentrcia, Toufik ; Mennouni, Abdelaziz

Journal of engineering mathematics, 2024-04, Vol.145 (1), Article 21 [Peer Reviewed Journal]

Dordrecht: Springer Netherlands

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