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Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to Capture Personalised Aortic Haemodynamics

2023-01-21

Abstract excerpt

Data driven, reduced order modelling has shown promise in tackling the challenges associated with computational and experimental hemodynamic models. In this work, we explore the use of Reduced Order Models (ROMs) to capture the main flow features in a patient-specific dissected aorta. We apply Proper Orthogonal Decomposition (POD) and Robust Principle Component Analysis (RPCA) on in vitro, hemodynamic data acquire...

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Literature Corpus work
5c22c4da-519c-5148-9cec-46e56baaa008
DOI
10.1101/2023.01.21.524933
Open publication

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Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to Capture Personalised Aortic HaemodynamicsDOI 10.1101/2023.01.21.524933
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