Pinning down the accuracy of physics-informed neural networks under laminar and turbulent-like aortic blood flow conditions.

Journal: Computers in biology and medicine
PMID: 39708495

Abstract

BACKGROUND: Physics-informed neural networks (PINNs) are increasingly being used to model cardiovascular blood flow. The accuracy of PINNs is dependent on flow complexity and could deteriorate in the presence of highly-dynamical blood flow conditions, but the extent of this relationship is currently unknown. Therefore, we investigated the accuracy and performance of PINNs under a range of blood flow conditions, from laminar to turbulent-like flows.

Authors

  • Arman Aghaee
    Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada.
  • M Owais Khan
    Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada. Electronic address: owaiskhan@torontomu.ca.

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