Andrea Manzoni
Orcid: 0000-0001-8277-2802
According to our database1,
Andrea Manzoni
authored at least 54 papers
between 2011 and 2024.
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Bibliography
2024
J. Sci. Comput., April, 2024
2023
J. Sci. Comput., November, 2023
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression.
Comput. Math. Appl., November, 2023
Neural Networks, April, 2023
Efficient and certified solution of parametrized one-way coupled problems through DEIM-based data projection across non-conforming interfaces.
Adv. Comput. Math., April, 2023
Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches.
Sensors, March, 2023
On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields.
CoRR, 2023
Nonlinear model order reduction for problems with microstructure using mesh informed neural networks.
CoRR, 2023
A staggered-in-time and non-conforming-in-space numerical framework for realistic cardiac electrophysiology outputs.
CoRR, 2023
Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks.
CoRR, 2023
Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition.
CoRR, 2023
A tissue-aware simulation framework for [18F]FLT spatiotemporal uptake in pancreatic ductal adenocarcinoma.
Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2023
2022
A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations.
Math. Comput., November, 2022
IEEE Trans. Robotics, 2022
Deep-HyROMnet: A Deep Learning-Based Operator Approximation for Hyper-Reduction of Nonlinear Parametrized PDEs.
J. Sci. Comput., 2022
Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions.
CoRR, 2022
Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches.
CoRR, 2022
Efficient approximation of cardiac mechanics through reduced order modeling with deep learning-based operator approximation.
CoRR, 2022
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models.
CoRR, 2022
2021
An Autoencoder-Based Deep Learning Approach for Load Identification in Structural Dynamics.
Sensors, 2021
A Comprehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs.
J. Sci. Comput., 2021
Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures.
CoRR, 2021
Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition.
CoRR, 2021
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models.
CoRR, 2021
Online structural health monitoring by model order reduction and deep learning algorithms.
CoRR, 2021
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities.
CoRR, 2021
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition.
CoRR, 2021
SUIHTER: A new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy.
CoRR, 2021
Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks.
Comput. Math. Appl., 2021
2020
Fully convolutional networks for structural health monitoring through multivariate time series classification.
Adv. Model. Simul. Eng. Sci., 2020
2019
Statistical closure modeling for reduced-order models of stationary systems by the ROMES method.
CoRR, 2019
Comput. Math. Appl., 2019
Hyper-reduced order models for parametrized unsteady Navier-Stokes equations on domains with variable shape.
Adv. Comput. Math., 2019
2018
SIAM J. Sci. Comput., 2018
2017
Efficient State/Parameter Estimation in Nonlinear Unsteady PDEs by a Reduced Basis Ensemble Kalman Filter.
SIAM/ASA J. Uncertain. Quantification, 2017
The cardiovascular system: Mathematical modelling, numerical algorithms and clinical applications.
Acta Numer., 2017
2016
Use of Operational Microwave Link Measurements for the Tomographic Reconstruction of 2-D Maps of Accumulated Rainfall.
IEEE Geosci. Remote. Sens. Lett., 2016
Accurate Solution of Bayesian Inverse Uncertainty Quantification Problems Combining Reduced Basis Methods and Reduction Error Models.
SIAM/ASA J. Uncertain. Quantification, 2016
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., 2016
2015
Efficient model reduction of parametrized systems by matrix discrete empirical interpolation.
J. Comput. Phys., 2015
Reduced basis approximation of parametrized optimal flow control problems for the Stokes equations.
Comput. Math. Appl., 2015
Heuristic strategies for the approximation of stability factors in quadratically nonlinear parametrized PDEs.
Adv. Comput. Math., 2015
2014
Shape Optimization by Free-Form Deformation: Existence Results and Numerical Solution for Stokes Flows.
J. Sci. Comput., 2014
2013
SIAM J. Sci. Comput., 2013
Reduced basis approximation and a posteriori error estimation for Stokes flows in parametrized geometries: roles of the inf-sup stability constants.
Numerische Mathematik, 2013
2012
A Reduced-Order Strategy for Solving Inverse Bayesian Shape Identification Problems in Physiological Flows.
Proceedings of the Modeling, Simulation and Optimization of Complex Processes, 2012
2011
Proceedings of the System Modeling and Optimization, 2011