activity
20172022
most citedA comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs

5 citations · 11 across the 9 of their papers we have counts for

collaborators

12 papers

math.NA2022

Efficient and certified solution of parametrized one-way coupled problems through DEIM-based data projection across non-conforming interfaces

Elena Zappon, Andrea Manzoni, Alfio Quarteroni

One of the major challenges of coupled problems is to manage nonconforming meshes at the interface between two models and/or domains, due to different numerical schemes or domains…

math.OC2022

Indirect Optimal Control of Advection-Diffusion Fields through Robotic Swarms

Carlo Sinigaglia, Andrea Manzoni, Francesco Braghin +1

In this paper, we consider the problem of optimally guiding a large-scale swarm of underwater vehicles that is tasked with the indirect control of an advection-diffusion environmen…

math.NA20221 cited

Efficient approximation of cardiac mechanics through reduced order modeling with deep learning-based operator approximation

Ludovica Cicci, Stefania Fresca, Andrea Manzoni +1

Reducing the computational time required by high-fidelity, full order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient…

math.NA20221 cited

Deep-HyROMnet: A deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs

Ludovica Cicci, Stefania Fresca, Andrea Manzoni

To speed-up the solution to parametrized differential problems, reduced order models (ROMs) have been developed over the years, including projection-based ROMs such as the reduced-…

math.NA20224 cited

Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models

Federico Fatone, Stefania Fresca, Andrea Manzoni

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional ROMs - built, e.g., exclusively through proper…

math.NA2021

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…