default search action
Andrea Manzoni
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j30]Simone Brivio, Stefania Fresca, Nicola Rares Franco, Andrea Manzoni
:
Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition. Adv. Comput. Math. 50(3): 33 (2024) - [j29]Nicola Rares Franco
On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields. Adv. Comput. Math. 50(5): 96 (2024) - [j28]Elena Zappon
A non-conforming-in-space numerical framework for realistic cardiac electrophysiological outputs. J. Comput. Phys. 502: 112815 (2024) - [j27]Elena Zappon
A Reduced Order Model for Domain Decompositions with Non-conforming Interfaces. J. Sci. Comput. 99(1): 22 (2024) - [i41]Aurelio Raffa Ugolini, Valentina Breschi, Andrea Manzoni, Mara Tanelli:
SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study. CoRR abs/2403.00578 (2024) - [i40]Luca Rosafalco, Paolo Conti, Andrea Manzoni, Stefano Mariani, Attilio Frangi:
EKF-SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics. CoRR abs/2404.07536 (2024) - [i39]Nicola Rares Franco, Andrea Manzoni, Paolo Zunino, Jan S. Hesthaven:
Deep orthogonal decomposition: a continuously adaptive data-driven approach to model order reduction. CoRR abs/2404.18841 (2024) - [i38]Simone Brivio, Stefania Fresca, Andrea Manzoni:
PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs. CoRR abs/2405.08558 (2024) - [i37]Matteo Torzoni, Andrea Manzoni, Stefano Mariani:
Enhancing Bayesian model updating in structural health monitoring via learnable mappings. CoRR abs/2405.13648 (2024) - [i36]Nicolò Botteghi, Paolo Motta, Andrea Manzoni, Paolo Zunino, Mengwu Guo:
Recurrent Deep Kernel Learning of Dynamical Systems. CoRR abs/2405.19785 (2024) - [i35]Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz:
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification. CoRR abs/2405.20905 (2024) - [i34]Nicola Farenga, Stefania Fresca, Simone Brivio, Andrea Manzoni:
On latent dynamics learning in nonlinear reduced order modeling. CoRR abs/2408.15183 (2024) - [i33]Matteo Tomasetto, Andrea Manzoni, Francesco Braghin:
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models. CoRR abs/2409.05709 (2024) - 2023
- [j26]Elena Zappon
Efficient and certified solution of parametrized one-way coupled problems through DEIM-based data projection across non-conforming interfaces. Adv. Comput. Math. 49(2): 21 (2023) - [j25]Ludovica Cicci
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression. Comput. Math. Appl. 149: 1-23 (2023) - [j24]Nicola Rares Franco
Mesh-Informed Neural Networks for Operator Learning in Finite Element Spaces. J. Sci. Comput. 97(2): 35 (2023) - [j23]Nicola Rares Franco
Approximation bounds for convolutional neural networks in operator learning. Neural Networks 161: 129-141 (2023) - [j22]Giorgio Gobat
Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches. Sensors 23(6): 3001 (2023) - [c3]Lara Cavinato, Jimin Hong, Stefan Reinhard, Martin Wartenberg, Paolo Zunino
A tissue-aware simulation framework for [18F]FLT spatiotemporal uptake in pancreatic ductal adenocarcinoma. CIBCB 2023: 1-9 - [i32]Ludovica Cicci, Stefania Fresca, Mengwu Guo, Andrea Manzoni, Paolo Zunino:
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression. CoRR abs/2302.08216 (2023) - [i31]Simone Brivio, Stefania Fresca, Nicola Rares Franco, Andrea Manzoni:
Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition. CoRR abs/2305.04680 (2023) - [i30]Matteo Torzoni, Marco Tezzele, Stefano Mariani, Andrea Manzoni, Karen E. Willcox:
A digital twin framework for civil engineering structures. CoRR abs/2308.01445 (2023) - [i29]Nicola Rares Franco, Stefania Fresca, Filippo Tombari, Andrea Manzoni:
Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks. CoRR abs/2308.01602 (2023) - [i28]Elena Zappon, Andrea Manzoni, Alfio Quarteroni:
A staggered-in-time and non-conforming-in-space numerical framework for realistic cardiac electrophysiology outputs. CoRR abs/2308.03884 (2023) - [i27]Paolo Conti, Mengwu Guo, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz:
Multi-fidelity reduced-order surrogate modeling. CoRR abs/2309.00325 (2023) - [i26]Piermario Vitullo, Alessio Colombo, Nicola Rares Franco, Andrea Manzoni, Paolo Zunino:
Nonlinear model order reduction for problems with microstructure using mesh informed neural networks. CoRR abs/2309.07815 (2023) - [i25]Nicola Rares Franco, Daniel Fraulin, Andrea Manzoni, Paolo Zunino:
On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields. CoRR abs/2310.12095 (2023) - 2022
- [j21]Ludovica Cicci
Deep-HyROMnet: A Deep Learning-Based Operator Approximation for Hyper-Reduction of Nonlinear Parametrized PDEs. J. Sci. Comput. 93(2): 57 (2022) - [j20]Nicola R. Franco
A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations. Math. Comput. 92(340): 483-524 (2022) - [j19]Carlo Sinigaglia
Density Control of Large-Scale Particles Swarm Through PDE-Constrained Optimization. IEEE Trans. Robotics 38(6): 3530-3549 (2022) - [i24]Federico Fatone, Stefania Fresca, Andrea Manzoni:
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models. CoRR abs/2201.10215 (2022) - [i23]Ludovica Cicci, Stefania Fresca, Andrea Manzoni:
Deep-HyROMnet: A deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs. CoRR abs/2202.02658 (2022) - [i22]Ludovica Cicci, Stefania Fresca, Andrea Manzoni, Alfio Quarteroni:
Efficient approximation of cardiac mechanics through reduced order modeling with deep learning-based operator approximation. CoRR abs/2202.03904 (2022) - [i21]Elena Zappon, Andrea Manzoni, Alfio Quarteroni:
Efficient and certified solution of parametrized one-way coupled problems through DEIM-based data projection across non-conforming interfaces. CoRR abs/2203.09226 (2022) - [i20]Nicola Rares Franco, Andrea Manzoni, Paolo Zunino:
Learning Operators with Mesh-Informed Neural Networks. CoRR abs/2203.11648 (2022) - [i19]Giorgio Gobat, Stefania Fresca
Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches. CoRR abs/2205.05928 (2022) - [i18]Elena Zappon, Andrea Manzoni, Paola Gervasio, Alfio Quarteroni:
A reduced order model for domain decompositions with non-conforming interfaces. CoRR abs/2206.09618 (2022) - [i17]Nicola Rares Franco, Stefania Fresca, Andrea Manzoni, Paolo Zunino:
Approximation bounds for convolutional neural networks in operator learning. CoRR abs/2207.01546 (2022) - [i16]Paolo Conti, Mengwu Guo, Andrea Manzoni, Jan S. Hesthaven:
Multi-fidelity surrogate modeling using long short-term memory networks. CoRR abs/2208.03115 (2022) - [i15]Paolo Conti, Giorgio Gobat, Stefania Fresca, Andrea Manzoni, Attilio Frangi:
Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions. CoRR abs/2211.06786 (2022) - 2021
- [j18]Matteo Salvador
Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks. Comput. Math. Appl. 104: 1-13 (2021) - [j17]Stefania Fresca
A Comprehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs. J. Sci. Comput. 87(2): 61 (2021) - [j16]Luca Rosafalco
An Autoencoder-Based Deep Learning Approach for Load Identification in Structural Dynamics. Sensors 21(12): 4207 (2021) - [i14]Nicola Parolini, Luca Dedè, Paola F. Antonietti, Giovanni Ardenghi, Andrea Manzoni, Edie Miglio, Andrea Pugliese, Marco Verani, Alfio Quarteroni:
SUIHTER: A new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy. CoRR abs/2101.03369 (2021) - [i13]Stefania Fresca, Andrea Manzoni:
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition. CoRR abs/2101.11845 (2021) - [i12]Michela Carlotta Massi, Nicola R. Franco, Francesca Ieva, Andrea Manzoni, Anna Maria Paganoni, Paolo Zunino:
Learning High-Order Interactions via Targeted Pattern Search. CoRR abs/2102.12974 (2021) - [i11]Mengwu Guo, Andrea Manzoni, Maurice Amendt, Paolo Conti, Jan S. Hesthaven:
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities. CoRR abs/2102.13403 (2021) - [i10]Nicola R. Franco, Andrea Manzoni, Paolo Zunino:
A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations. CoRR abs/2103.06183 (2021) - [i9]Luca Rosafalco, Matteo Torzoni, Andrea Manzoni, Stefano Mariani, Alberto Corigliano:
Online structural health monitoring by model order reduction and deep learning algorithms. CoRR abs/2103.14328 (2021) - [i8]Matteo Salvador, Luca Dedè, Andrea Manzoni:
Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks. CoRR abs/2103.17152 (2021) - [i7]Stefania Fresca, Andrea Manzoni:
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models. CoRR abs/2106.05722 (2021) - [i6]Giorgio Gobat, Andrea Opreni, Stefania Fresca, Andrea Manzoni, Attilio Frangi:
Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition. CoRR abs/2109.12184 (2021) - [i5]Stefania Fresca, Giorgio Gobat, Patrick Fedeli, Attilio Frangi, Andrea Manzoni:
Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures. CoRR abs/2111.12511 (2021) - 2020
- [j15]Luca Rosafalco
Fully convolutional networks for structural health monitoring through multivariate time series classification. Adv. Model. Simul. Eng. Sci. 7(1): 38 (2020) - [i4]Stefania Fresca, Luca Dedè, Andrea Manzoni:
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs. CoRR abs/2001.04001 (2020) - [i3]Luca Rosafalco, Andrea Manzoni, Stefano Mariani, Alberto Corigliano:
Fully convolutional networks for structural health monitoring through multivariate time series classification. CoRR abs/2002.07032 (2020) - [i2]Stefania Fresca, Andrea Manzoni, Luca Dedè, Alfio Quarteroni:
Deep learning-based reduced order models in cardiac electrophysiology. CoRR abs/2006.03040 (2020)
2010 – 2019
- 2019
- [j14]Niccolò Dal Santo, Andrea Manzoni
Hyper-reduced order models for parametrized unsteady Navier-Stokes equations on domains with variable shape. Adv. Comput. Math. 45(5): 2463-2501 (2019) - [j13]Niccolò Dal Santo, Simone Deparis
Multi space reduced basis preconditioners for parametrized Stokes equations. Comput. Math. Appl. 77(6): 1583-1604 (2019) - [i1]Stefano Pagani, Andrea Manzoni, Kevin Carlberg:
Statistical closure modeling for reduced-order models of stationary systems by the ROMES method. CoRR abs/1901.02792 (2019) - 2018
- [j12]Niccolò Dal Santo, Simone Deparis
Multi Space Reduced Basis Preconditioners for Large-Scale Parametrized PDEs. SIAM J. Sci. Comput. 40(2) (2018) - 2017
- [j11]Alfio Quarteroni
The cardiovascular system: Mathematical modelling, numerical algorithms and clinical applications. Acta Numer. 26: 365-590 (2017) - [j10]Stefano Pagani, Andrea Manzoni
Efficient State/Parameter Estimation in Nonlinear Unsteady PDEs by a Reduced Basis Ensemble Kalman Filter. SIAM/ASA J. Uncertain. Quantification 5(1): 890-921 (2017) - 2016
- [j9]Francesco Ballarin
Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a POD-Galerkin method and a vascular shape parametrization. J. Comput. Phys. 315: 609-628 (2016) - [j8]Andrea Manzoni
Accurate Solution of Bayesian Inverse Uncertainty Quantification Problems Combining Reduced Basis Methods and Reduction Error Models. SIAM/ASA J. Uncertain. Quantification 4(1): 380-412 (2016) - [j7]Michele d'Amico
Use of Operational Microwave Link Measurements for the Tomographic Reconstruction of 2-D Maps of Accumulated Rainfall. IEEE Geosci. Remote. Sens. Lett. 13(12): 1827-1831 (2016) - 2015
- [j6]Andrea Manzoni
Heuristic strategies for the approximation of stability factors in quadratically nonlinear parametrized PDEs. Adv. Comput. Math. 41(5): 1255-1288 (2015) - [j5]Federico Negri, Andrea Manzoni
Reduced basis approximation of parametrized optimal flow control problems for the Stokes equations. Comput. Math. Appl. 69(4): 319-336 (2015) - [j4]Federico Negri, Andrea Manzoni
Efficient model reduction of parametrized systems by matrix discrete empirical interpolation. J. Comput. Phys. 303: 431-454 (2015) - 2014
- [j3]Francesco Ballarin
Shape Optimization by Free-Form Deformation: Existence Results and Numerical Solution for Stokes Flows. J. Sci. Comput. 60(3): 537-563 (2014) - 2013
- [j2]Gianluigi Rozza
Reduced basis approximation and a posteriori error estimation for Stokes flows in parametrized geometries: roles of the inf-sup stability constants. Numerische Mathematik 125(1): 115-152 (2013) - [j1]Federico Negri, Gianluigi Rozza
Reduced Basis Method for Parametrized Elliptic Optimal Control Problems. SIAM J. Sci. Comput. 35(5) (2013) - 2012
- [c2]Andrea Manzoni, Toni Lassila, Alfio Quarteroni, Gianluigi Rozza:
A Reduced-Order Strategy for Solving Inverse Bayesian Shape Identification Problems in Physiological Flows. HPSC 2012: 145-155 - 2011
- [c1]Toni Lassila
Reduction Strategies for Shape Dependent Inverse Problems in Haemodynamics. System Modelling and Optimization 2011: 397-406
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-22 21:19 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: