Physics-informed neural networks for parameter estimation in blood flow models.

Journal: Computers in biology and medicine
PMID: 38879935

Abstract

BACKGROUND: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving inverse problems, especially in cases where no complete information about the system is known and scatter measurements are available. This is especially useful in hemodynamics since the boundary information is often difficult to model, and high-quality blood flow measurements are generally hard to obtain.

Authors

  • Jeremías Garay
    Department of Mechanical and Metallurgical Engineering, Pontificia Universidad Católica de Chile, Chile; Center of Biomedical Imaging, Pontificia Universidad Católica de Chile, Chile; Millennium Institute for Intelligent Healthcare Engineering (iHealth), Chile.
  • Jocelyn Dunstan
    Johns Hopkins University, USA; University of Chile, Chile.
  • Sergio Uribe
  • Francisco Sahli Costabal
    Department of Mechanical and Metallurgical Engineering, Pontificia Universidad Católica de Chile, Chile; Millennium Institute for Intelligent Healthcare Engineering (iHealth), Chile; Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Chile. Electronic address: fsc@ing.puc.cl.

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