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Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
Fueled by breakthrough technology developments, the biological, biomedical, and
behavioral sciences are now collecting more data than ever before. There is a critical need …
behavioral sciences are now collecting more data than ever before. There is a critical need …
An audit of uncertainty in multi-scale cardiac electrophysiology models
Models of electrical activation and recovery in cardiac cells and tissue have become
valuable research tools, and are beginning to be used in safety-critical applications …
valuable research tools, and are beginning to be used in safety-critical applications …
[HTML][HTML] A machine learning method for real-time numerical simulations of cardiac electromechanics
We propose a machine learning-based method to build a system of differential equations
that approximates the dynamics of 3D electromechanical models for the human heart …
that approximates the dynamics of 3D electromechanical models for the human heart …
Deep learning-based reduced order models in cardiac electrophysiology
Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies
on the numerical approximation of coupled nonlinear dynamical systems. These systems …
on the numerical approximation of coupled nonlinear dynamical systems. These systems …
Multiscale modeling of lung mechanics: from alveolar microstructure to pulmonary function
The mechanical behavior of the lungs has long been associated with the structural
properties of alveoli in pulmonary medicine. However, this structure-function relationship …
properties of alveoli in pulmonary medicine. However, this structure-function relationship …
[HTML][HTML] Sensitivity analysis of a strongly-coupled human-based electromechanical cardiac model: Effect of mechanical parameters on physiologically relevant …
The human heart beats as a result of multiscale nonlinear dynamics coupling subcellular to
whole organ processes, achieving electrophysiologically-driven mechanical contraction …
whole organ processes, achieving electrophysiologically-driven mechanical contraction …
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models
Abstract Machine learning techniques typically rely on large datasets to create accurate
classifiers. However, there are situations when data is scarce and expensive to acquire. This …
classifiers. However, there are situations when data is scarce and expensive to acquire. This …
Uncertainty quantification and sensitivity analysis of left ventricular function during the full cardiac cycle
Patient-specific computer simulations can be a powerful tool in clinical applications, hel**
in diagnostics and the development of new treatments. However, its practical use depends …
in diagnostics and the development of new treatments. However, its practical use depends …
Numerical approximation of parametrized problems in cardiac electrophysiology by a local reduced basis method
The efficient solution of coupled PDEs/ODEs problems arising in cardiac electrophysiology
is of key importance whenever interested to study the electrical behavior of the tissue for …
is of key importance whenever interested to study the electrical behavior of the tissue for …
Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning
We present a new, computationally efficient framework to perform forward uncertainty
quantification (UQ) in cardiac electrophysiology. We consider the monodomain model to …
quantification (UQ) in cardiac electrophysiology. We consider the monodomain model to …