KASHTANOVA , Victoriya

A current major scientific challenge consists in combining the versatility of intensive data-based approaches with the physically grounded modelling approaches developed in scientific fields such as biophysics. Biophysical modelling of the human heart offers a well-developed framework for introducing physiology into the predictive analysis of...

A current major scientific challenge consists in combining the versatility of intensive data-based approaches with the physically grounded modelling approaches developed in scientific fields such as biophysics. Biophysical modelling of the human heart offers a well-developed framework for introducing physiology into the predictive analysis of...

Biophysically detailed mathematical modeling of cardiac electrophysiology is often computationally demanding, for example, when solving problems for various patient pathological conditions. Furthermore, it is still difficult to reduce the discrepancy between the output of idealized mathematical models and clinical measurements, which are...

Cardiac electrophysiology models achieved good progress in simulating cardiac electrical activity. However, it is still challenging to leverage clinical measurements due to the discrepancy between idealised models and patient-specific conditions. In the last few years, data-driven machine learning methods have been actively used to learn...

Modelling complex systems, like the human heart, has made great progress over the last decades. Patient-specific models, called 'digital twins', can aid in diagnosing arrhythmias and personalizing treatments. However, building highly accurate predictive heart models requires a delicate balance between mathematical complexity, parameterization...

Imaging the electrical activity of the heart can be achieved with invasive catheterisation. However, the resulting data are sparse and noisy. Mathematical modelling of cardiac electrophysiology can help the analysis but solving the associated mathematical systems can become unfeasible. It is often computationally demanding, for instance when...