The cardiovascular system: mathematical modelling, numerical algorithms and clinical applications
Mathematical and numerical modelling of the cardiovascular system is a research topic that
has attracted remarkable interest from the mathematical community because of its intrinsic …
has attracted remarkable interest from the mathematical community because of its intrinsic …
Machine learning for cardiovascular biomechanics modeling: challenges and beyond
Recent progress in machine learning (ML), together with advanced computational power,
have provided new research opportunities in cardiovascular modeling. While classifying …
have provided new research opportunities in cardiovascular modeling. While classifying …
[LIVRE][B] Certified reduced basis methods for parametrized partial differential equations
During the past decade, reduced order modeling has attracted growing interest in
computational science and engineering. It now plays an important role in delivering high …
computational science and engineering. It now plays an important role in delivering high …
Supremizer stabilization of POD–Galerkin approximation of parametrized steady incompressible Navier–Stokes equations
Supremizer stabilization of POD–Galerkin approximation of parametrized steady
incompressible Navier–Stokes equations - Ballarin - 2015 - International Journal for Numerical …
incompressible Navier–Stokes equations - Ballarin - 2015 - International Journal for Numerical …
Persistent homology analysis of protein structure, flexibility, and folding
Proteins are the most important biomolecules for living organisms. The understanding of
protein structure, function, dynamics, and transport is one of the most challenging tasks in …
protein structure, function, dynamics, and transport is one of the most challenging tasks in …
[LIVRE][B] Mathematical modelling of the human cardiovascular system: data, numerical approximation, clinical applications
Mathematical and numerical modelling of the human cardiovascular system has attracted
remarkable research interest due to its intrinsic mathematical difficulty and the increasing …
remarkable research interest due to its intrinsic mathematical difficulty and the increasing …
[HTML][HTML] Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks
We present a parametric physics-informed neural network for the simulation of personalised
left-ventricular biomechanics. The neural network is constrained to the biophysical problem …
left-ventricular biomechanics. The neural network is constrained to the biophysical problem …
A registration method for model order reduction: data compression and geometry reduction
T Taddei - SIAM Journal on Scientific Computing, 2020 - SIAM
We propose a general---ie, independent of the underlying equation---registration method for
parameterized model order reduction. Given the spatial domain Ω⊂R^d and the manifold …
parameterized model order reduction. Given the spatial domain Ω⊂R^d and the manifold …
Efficient model reduction of parametrized systems by matrix discrete empirical interpolation
In this work, we apply a Matrix version of the so-called Discrete Empirical Interpolation
(MDEIM) for the efficient reduction of nonaffine parametrized systems arising from the …
(MDEIM) for the efficient reduction of nonaffine parametrized systems arising from the …
Automated generation of 0D and 1D reduced‐order models of patient‐specific blood flow
Abstract Three‐dimensional (3D) cardiovascular fluid dynamics simulations typically require
hours to days of computing time on a high‐performance computing cluster. One …
hours to days of computing time on a high‐performance computing cluster. One …