In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task
depending on many external factors. The topology of the scene and the interactions …

Data science models, although successful in a number of commercial domains, have had
limited applicability in scientific problems involving complex physical phenomena. Theory …

Abstract Although Deep Neural Networks (DNNs) have great generalization and prediction
capabilities, their functioning does not allow a detailed explanation of their behavior …

Physics-based simulations are often used to model and understand complex physical
systems in domains such as fluid dynamics. Such simulations, although used frequently …

Influenza-like illness (ILI) places a heavy social and economic burden on our society.
Traditionally, ILI surveillance data are updated weekly and provided at a spatially coarse …

Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac
function in a non-invasive manner, making them a promising approach for personalized …

Data-driven models (DDMs) are extensively used in environmental modeling yet encounter
obstacles stemming from limited training data and potential discrepancies with physical …

Background Myocardial infarction (MI) scar constitutes a substrate for ventricular tachycardia
(VT), and an accurate delineation of infarct scar may help to identify reentrant circuits and …

Cardiac digital twins are personalized virtual representations used to understand complex
heart mechanisms. Solving the ECG inverse problem is crucial for accurate virtual heart …