XXIV Symposium on Atomic, Cluster and Surface Physics
Augmenting physics-based models by means of Scientific Machine Learning methods in Computational Cardiology
The Synergy of Physics-Informed and Machine Learning Techniques for Model Personalization
A Neural Network Approach for Reconstruction of Cardiac Electroanatomical Maps
Physics-informed neural networks for patient-specific predictions in the cardiovascular system
The payment of a five-year bill
Secular growths and their relation to equilibrium states in perturbative QFT
Visiting Chair from the Collège de France – Professor Francoise Combes
Integration by parts formula and quantum field theory
Bayesian Cluster Analysis: An Application to Investigate Neuronal Connectivity Patterns
Donne di potere
Graphs and pairings of elliptic curves
Existence theory for perimeter functionals with measure data under isoperimetric conditions