This study uses Bayesian inference to quantify the uncertainty of model parameters and
haemodynamic predictions in a one-dimensional pulmonary circulation model based on an …

There have been impressive advances in the physical and mathematical modelling of
complex physiological systems in the last few decades, with the potential to revolutionise …

Introduction: Computational models of the cardiovascular system are widely used to
simulate cardiac (dys) function. Personalization of such models for patient-specific …

The past few decades have witnessed an explosive synergy between physics and the life
sciences. In particular, physical modelling in medicine and physiology is a topical research …

This study creates a physiologically realistic virtual patient database (VPD), representing the
human arterial system, for the primary purpose of studying the effects of arterial disease on …

Probabilistic estimation of cardiac electrophysiological model parameters serves an
important step toward model personalization and uncertain quantification. The expensive …

We propose to accelerate Hamiltonian and Lagrangian Monte Carlo algorithms by coupling
them with Gaussian processes for emulation of the log unnormalised posterior distribution …

The current study employs state-of-the-art optimisation methods for estimation of unknown
parameters in a mathematical model of highly non-linear partial differential equations …

Abstract Pulmonary hypertension (PH), ie, high blood pressure in the lungs, is a serious
medical condition that can damage the right ventricle of the heart and ultimately lead to heart …

We have recently seen impressive methodological developments in quantitative
cardiovascular physiology and pathophysiology, with novel mathematical models for the …