Amirhossein Arzani

Emailamir.arzaninauedu

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30 Article
3 Conference contribution
2 Review article
1 Editorial

Current state-of-the-art and utilities of machine learning for detection, monitoring, growth prediction, rupture risk assessment, and post-surgical management of abdominal aortic aneurysms
Baek, S. & Arzani, A., Jun 2022, In: Applications in Engineering Science. 10, 100097.
Research output: Contribution to journal › Article › peer-review

Open Access
Guiding the Prostatic Artery Embolization Procedure With Computational Fluid Dynamics
Mahmoudi, M., Jennings, C., Pereira, K., Hall, A. F. & Arzani, A., Nov 1 2022, In: Journal of Biomechanical Engineering. 144, 11, 111004 EN.
Research output: Contribution to journal › Article › peer-review
Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond
Arzani, A., Wang, J. X., Sacks, M. S. & Shadden, S. C., Jun 2022, In: Annals of Biomedical Engineering. 50, 6, p. 615-627 13 p.
Research output: Contribution to journal › Review article › peer-review
Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks
Aliakbari, M., Mahmoudi, M., Vadasz, P. & Arzani, A., Aug 2022, In: International Journal of Heat and Fluid Flow. 96, 109002.
Research output: Contribution to journal › Article › peer-review
1 Scopus citations
Data-driven cardiovascular flow modelling: Examples and opportunities
Arzani, A. & Dawson, S. T. M., Feb 2021, In: Journal of the Royal Society Interface. 18, 175, 20200802.
Research output: Contribution to journal › Review article › peer-review
14 Scopus citations

Supplementary material from "Accounting for residence-time in blood rheology models: do we really need non-Newtonian blood flow modelling in large arteries?"
Arzani, A. (Contributor), figshare Academic Research System, 2018
DOI: https://doi.org/10.6084/m9.figshare.c.4226417.v1, https://doi.org/10.6084%2Fm9.figshare.c.4226417.v1
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Amirhossein Arzani

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Current state-of-the-art and utilities of machine learning for detection, monitoring, growth prediction, rupture risk assessment, and post-surgical management of abdominal aortic aneurysms

Guiding the Prostatic Artery Embolization Procedure With Computational Fluid Dynamics

Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond

Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks

Data-driven cardiovascular flow modelling: Examples and opportunities

Supplementary material from "Accounting for residence-time in blood rheology models: do we really need non-Newtonian blood flow modelling in large arteries?"

Amirhossein Arzani

Fingerprint

Network

Research output

Current state-of-the-art and utilities of machine learning for detection, monitoring, growth prediction, rupture risk assessment, and post-surgical management of abdominal aortic aneurysms

Guiding the Prostatic Artery Embolization Procedure With Computational Fluid Dynamics

Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond

Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks

Data-driven cardiovascular flow modelling: Examples and opportunities

Datasets

Supplementary material from "Accounting for residence-time in blood rheology models: do we really need non-Newtonian blood flow modelling in large arteries?"