Luca Dedè

MOX laboratory, Department of Mathematics, Politecnico di Milano

Bio: Luca Dedè is Associate Professor of Numerical Analysis at the MOX laboratory, Department of Mathematics, Politecnico di Milano. His research focuses on developing mathematical models and numerical methods for the simulation of problems from various industrial and life science applications. In particular, he is actively engaged in the emerging fields of Computational and Precision Medicine, with a focus on applications related to the human heart and cardiac digital twinning technology. His research contributions and expertise include mathematical modelling, numerical methods for partial differential equations, Finite Element and isogeometric methods, methods for High Performace Computing, Scientific Machine Learning. He has co-authored over 100 peer-reviewed articles in international journals and one book.

KEYNOTE

Learning Spatio-Temporal Dynamics with Latent Dynamics Networks for Constructing Cardiac Digital Twins

Abstract: The efficient modeling of spatio-temporal dynamics is crucial for advancing scientific computing and enabling personalized medicine. This talk focuses on recent developments in Latent Dynamics Networks (LDNets), which provide lightweight, accurate, and scalable solutions for complex dynamical systems with spatio-temporal features. These advancements bridge traditional physics-based and data-driven approaches, enabling real-time, scalable simulations for cardiac modeling and beyond. A key application of this talk is in cardiac digital twins, where LDNets facilitate real-time whole-heart electromechanical simulations. By learning a compact representation of cardiac models, we demonstrate through meaningful examples how these models dramatically reduce computational costs while retaining high fidelity. This enables efficient global sensitivity analysis, parameter estimation, and uncertainty quantification, all on standard computational hardware. The potential of LDNets in cardiac digital twins underscores their transformative impact on personalized medicine by providing faster, more accessible simulations without compromising accuracy.

Page updated

Google Sites

Report abuse