130 citations · 579 across the 34 of their papers we have counts for
11 papers · 1 filter
Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures
Stefania Fresca, Giorgio Gobat, Patrick Fedeli +2
We propose a non-intrusive Deep Learning-based Reduced Order Model (DL-ROM) capable of capturing the complex dynamics of mechanical systems showing inertia and geometric nonlineari…
Fast active thermal cloaking through PDE-constrained optimization and reduced-order modeling
Carlo Sinigaglia, Davide Enrico Quadrelli, Andrea Manzoni +1
In this paper we show how to efficiently achieve thermal cloaking from a computational standpoint in several virtual scenarios by controlling a distribution of active heat sources.…
Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition
Gobat G., Opreni A., Fresca S. +2
We apply the Proper Orthogonal Decomposition (POD) method for the efficient simulation of several scenarios undergone by Micro-Electro-Mechanical-Systems, involving nonlinearites o…
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models
Stefania Fresca, Andrea Manzoni
Simulating fluid flows in different virtual scenarios is of key importance in engineering applications. However, high-fidelity, full-order models relying, e.g., on the finite eleme…
Density control of large-scale particles swarm through PDE-constrained optimization
Carlo Sinigaglia, Andrea Manzoni, Francesco Braghin
We describe in this paper an optimal control strategy for shaping a large-scale swarm of particles using boundary global actuation. This problem arises as a key challenge in many s…
Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks
Matteo Salvador, Luca Dede', Andrea Manzoni
We propose a nonlinear reduced basis method for the efficient approximation of parametrized partial differential equations (PDEs), exploiting kernel proper orthogonal decomposition…
Online structural health monitoring by model order reduction and deep learning algorithms
Luca Rosafalco, Matteo Torzoni, Andrea Manzoni +2
Within a structural health monitoring (SHM) framework, we propose a simulation-based classification strategy to move towards online damage localization. The procedure combines para…
A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations
Nicola R. Franco, Andrea Manzoni, Paolo Zunino
Within the framework of parameter dependent PDEs, we develop a constructive approach based on Deep Neural Networks for the efficient approximation of the parameter-to-solution map.…
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities
Mengwu Guo, Andrea Manzoni, Maurice Amendt +2
Highly accurate numerical or physical experiments are often time-consuming or expensive to obtain. When time or budget restrictions prohibit the generation of additional data, the…
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
Stefania Fresca, Andrea Manzoni
Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional reduced order models (ROMs) - built, e.g., thro…
SUIHTER: A new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy
Nicola Parolini, Luca Dede', Paola F. Antonietti +6
The COVID-19 epidemic is the last of a long list of pandemics that have affected humankind in the last century. In this paper, we propose a novel mathematical epidemiological model…