91 citations · 258 across the 24 of their papers we have counts for
8 papers · 2 filters
Multi-fidelity surrogate modeling using long short-term memory networks
Paolo Conti, Mengwu Guo, Andrea Manzoni +1
When evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face the trade-off between accuracy and efficiency. Especially for para…
Approximation bounds for convolutional neural networks in operator learning
Nicola Rares Franco, Stefania Fresca, Andrea Manzoni +1
Recently, deep Convolutional Neural Networks (CNNs) have proven to be successful when employed in areas such as reduced order modeling of parametrized PDEs. Despite their accuracy…
A reduced order model for domain decompositions with non-conforming interfaces
Elena Zappon, Andrea Manzoni, Paola Gervasio +1
In this paper, we propose a reduced-order modeling strategy for two-way Dirichlet-Neumann parametric coupled problems solved with domain-decomposition (DD) sub-structuring methods.…
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…
Mesh-Informed Neural Networks for Operator Learning in Finite Element Spaces
Nicola Rares Franco, Andrea Manzoni, Paolo Zunino
Thanks to their universal approximation properties and new efficient training strategies, Deep Neural Networks are becoming a valuable tool for the approximation of mathematical op…
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…