Data-driven cardiovascular flow modelling: Examples and opportunities

Amirhossein Arzani, Scott T.M. Dawson

Research output: Contribution to journalReview articlepeer-review

35 Scopus citations


High-fidelity blood flow modelling is crucial for enhancing our understanding of cardiovascular disease. Despite significant advances in computational and experimental characterization of blood flow, the knowledge that we can acquire from such investigations remains limited by the presence of uncertainty in parameters, low resolution, and measurement noise. Additionally, extracting useful information from these datasets is challenging. Data-driven modelling techniques have the potential to overcome these challenges and transform cardiovascular flow modelling. Here, we review several data-driven modelling techniques, highlight the common ideas and principles that emerge across numerous such techniques, and provide illustrative examples of how they could be used in the context of cardiovascular fluid mechanics. In particular, we discuss principal component analysis (PCA), robust PCA, compressed sensing, the Kalman filter for data assimilation, low-rank data recovery, and several additional methods for reduced-order modelling of cardiovascular flows, including the dynamic mode decomposition and the sparse identification of nonlinear dynamics. All techniques are presented in the context of cardiovascular flows with simple examples. These data-driven modelling techniques have the potential to transform computational and experimental cardiovascular research, and we discuss challenges and opportunities in applying these techniques in the field, looking ultimately towards data-driven patient-specific blood flow modelling.

Original languageEnglish (US)
Article number20200802
JournalJournal of the Royal Society Interface
Issue number175
StatePublished - Feb 2021
Externally publishedYes


  • blood flow
  • data science
  • data-driven dynamical systems
  • haemodynamics
  • reduced-order modelling
  • sparse sensing

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Bioengineering
  • Biomaterials
  • Biochemistry
  • Biomedical Engineering


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