2 citations · 2 across the 4 of their papers we have counts for
6 papers
Multiscale Mixed-Dimensional Simulation via Domain Decomposition and Non-Intrusive Neural Model Order Reduction
Nunzio Dimola, Piermario Vitullo, Paolo Zunino
Many computational models arising in science and engineering exhibit a multiscale structure that makes the assembly or direct solution of the global problem computationally prohibi…
Physics-Informed Learning of Microvascular Flow Models using Graph Neural Networks
Paolo Botta, Piermario Vitullo, Thomas Ventimiglia +2
The simulation of microcirculatory blood flow in realistic vascular architectures poses significant challenges due to the multiscale nature of the problem and the topological compl…
Neural Preconditioning via Krylov Subspace Geometry
Nunzio Dimola, Alessandro Coclite, Paolo Zunino
We propose a geometry-aware strategy for training neural preconditioners tailored to parametrized linear systems arising from the discretization of mixed-dimensional partial differ…
Mathematical modeling and sensitivity analysis of hypoxia-activated drugs
Alessandro Coclite, Riccardo Montanelli Eccher, Luca Possenti +2
Hypoxia-activated prodrugs offer a promising strategy for targeting oxygen-deficient regions in solid tumors, which are often resistant to conventional therapies. However, modeling…
Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy
Piermario Vitullo, Nicola Rares Franco, Paolo Zunino
Forward uncertainty quantification (UQ) for partial differential equations is a many-query task that requires a significant number of model evaluations. The objective of this work…
Deep learning based reduced order modeling of Darcy flow systems with local mass conservation
Wietse M. Boon, Nicola R. Franco, Alessio Fumagalli +1
We propose a new reduced order modeling strategy for tackling parametrized Partial Differential Equations (PDEs) with linear constraints, in particular Darcy flow systems in which…