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DEHGHANI Hamidreza

Main Referenced Co-authors
ZILIAN, Andreas  (10)
Merodio, Jose (2)
Font, Alejandro (1)
Holzapfel, Gerhard (1)
Jha, Niraj Kumar (1)
Main Referenced Keywords
Data-driven computational mechanics (3); Poroelasticity (3); AI in mechanics (2); Artificial Neural Network (2); Asymptotic homogenisation (2);
Main Referenced Unit & Research Centers
University of Luxembourg: Institute of Computational Engineering and Sciences (2)
University of Luxembourg, Institute of Computational Engineering and Sciences (1)
University of Luxembourg: Institute of Computational Engineering (1)
Main Referenced Disciplines
Civil engineering (9)
Engineering, computing & technology: Multidisciplinary, general & others (9)
Materials science & engineering (7)
Mechanical engineering (4)
Computer science (2)

Publications (total 12)

The most downloaded
125 downloads
DEHGHANI, H., & ZILIAN, A. (2021). Data science meets computational mechanics. University of Luxembourg. https://orbilu.uni.lu/handle/10993/47309 https://hdl.handle.net/10993/47309

The most cited

26 citations (Scopus®)

Font, A., Jha, N. K., DEHGHANI, H., Reinoso, J., & Merodio, J. (January 2021). Modelling of residually stressed, extended and inflated cylinders with application to aneurysms. Mechanics Research Communications, 111, 103346. doi:10.1016/j.mechrescom.2020.103643 https://hdl.handle.net/10993/45253

DEHGHANI, H., Holzapfel, G., MITTELBRONN, M., & ZILIAN, A. (27 February 2024). Cell adhesion affects the properties of interstitial fluid flow: A study using multiscale poroelastic composite modeling. Journal of the Mechanical Behavior of Biomedical Materials, 153. doi:10.1016/j.jmbbm.2024.106486
Peer Reviewed verified by ORBi

DEHGHANI, H., & ZILIAN, A. (2023). Finite strain poro-hyperelasticity: an asymptotic multi-scale ALE-FSI approach supported by ANNs. Computational Mechanics. doi:10.1007/s00466-022-02262-y
Peer Reviewed verified by ORBi

DEHGHANI, H., & ZILIAN, A. (January 2022). AI-supported Modelling of Brain tissue as Soft Multiscale Multiphysics (Poroelastic) medium [Paper presentation]. Interdisciplinary meeting for brain tissue modelling.

DEHGHANI, H., & ZILIAN, A. (21 May 2021). AI-aided, incremental numerical approach for fi nite strain poroelasticity: On the brain tissue deformation [Paper presentation]. SIAM Conference on Mathematical Aspects of Materials Science.

DEHGHANI, H., & ZILIAN, A. (2021). ANN-aided incremental multiscale-remodelling-based finite strain poroelasticity. Computational Mechanics. doi:10.1007/s00466-021-02023-3
Peer Reviewed verified by ORBi

DEHGHANI, H., & ZILIAN, A. (2021). Data science meets computational mechanics. University of Luxembourg. https://orbilu.uni.lu/handle/10993/47309

Font, A., Jha, N. K., DEHGHANI, H., Reinoso, J., & Merodio, J. (January 2021). Modelling of residually stressed, extended and inflated cylinders with application to aneurysms. Mechanics Research Communications, 111, 103346. doi:10.1016/j.mechrescom.2020.103643
Peer Reviewed verified by ORBi

DEHGHANI, H., & ZILIAN, A. (2020). A hybrid MGA-MSGD ANN training approach for approximate solution of linear elliptic PDEs. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/45257.

DEHGHANI, H., & ZILIAN, A. (September 2020). Poroelastic model parameter identification using artificial neural networks: on the effects of heterogeneous porosity and solid matrix Poisson ratio. Computational Mechanics, 66, 625-649. doi:10.1007/s00466-020-01868-4
Peer Reviewed verified by ORBi

DEHGHANI, H., & ZILIAN, A. (13 January 2020). Continuous solution of poroelastic problems using Artificial Neural Networks [Paper presentation]. Team meeting of Andreas Zilian, Belval, Luxembourg.

DEHGHANI, H., Noll, I., Penta, R., Menzel, A., & Merodio, J. (2020). The role of microscale solid matrix compressibility on the mechanical behaviour of poroelastic materials. European Journal of Mechanics. A, Solids, 83, 103996. doi:10.1016/j.euromechsol.2020.103996
Peer Reviewed verified by ORBi

DEHGHANI, H., & ZILIAN, A. (13 November 2019). Poroelastic material characterisation by means of Artificial Neural Network [Paper presentation]. Team meeting of Andreas Zilian, Belval, Luxembourg.

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