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Le informazioni sulla didattica, sulla ricerca e sui compiti istituzionali riportate in questa pagina sono certificate dall'Ateneo; ulteriori informazioni, redatte a cura del docente, sono disponibili sulla pagina web personale e nel curriculum vitae indicati nella scheda.
English
Informazioni
DocenteManzoni Andrea
QualificaProfessore associato a tempo pieno
Dipartimento d'afferenzaDipartimento di Matematica
Settore Scientifico DisciplinareMATH-05/A - Analisi Numerica
Curriculum VitaeScarica il CV (328.24Kb - 08/01/2025)
OrcIDhttps://orcid.org/0000-0001-8277-2802

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E-mailandrea1.manzoni@polimi.it
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Fonte dati: RE.PUBLIC@POLIMI - Research Publications at Politecnico di Milano

Elenco delle pubblicazioni e dei prodotti della ricerca per l'anno 2025 (Mostra tutto | Nascondi tutto)
Tipologia Titolo Pubblicazione/Prodotto
Abstract in Atti di convegno
Online learning of time-varying dynamic systems with EKF-SINDy (Mostra >>)
Predictive Digital Twins for Congestive Heart Failure Patients (Mostra >>)
Predictive digital twins for health monitoring: From structural safety to personalized medicine (Mostra >>)
Articoli su riviste
On latent dynamics learning in nonlinear reduced order modeling (Mostra >>)
Online learning in bifurcating dynamic systems via SINDy and Kalman filtering (Mostra >>)


Elenco delle pubblicazioni e dei prodotti della ricerca per l'anno 2024 (Mostra tutto | Nascondi tutto)
Tipologia Titolo Pubblicazione/Prodotto
Abstract in Atti di convegno
Predictive Digital Twins for Optimized Management and Maintenance of Civil Structures (Mostra >>)
Predictive digital twins of engineering structures using neural networks and probabilistic graphical models (Mostra >>)
Poster
A predictive Digital Twin framework for risk assessment in congestive heart failure (Mostra >>)
Contributi su volumi (Capitolo o Saggio)
Deep Learning-Based Reduced Order Models for Cardiac Electrophysiology (Mostra >>)
Contributo in Atti di convegno
SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study (Mostra >>)
Articoli su riviste
A Reduced Order Model for Domain Decompositions with Non-conforming Interfaces (Mostra >>)
A digital twin framework for civil engineering structures (Mostra >>)
A non-conforming-in-space numerical framework for realistic cardiac electrophysiological outputs (Mostra >>)
An optimal control strategy to design passive thermal cloaks of arbitrary shape (Mostra >>)
EKF–SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics (Mostra >>)
Efficient approximation of cardiac mechanics through reduced‐order modeling with deep learning‐based operator approximation (Mostra >>)
Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition (Mostra >>)
Multi-fidelity reduced-order surrogate modelling (Mostra >>)
Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers (Mostra >>)
Nonlinear model order reduction for problems with microstructure using mesh informed neural networks (Mostra >>)
On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields (Mostra >>)
PTPI-DL-ROMs: Pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Mostra >>)
Unveiling the biological side of PET-derived biomarkers: a simulation-based approach applied to PDAC assessment (Mostra >>)


Elenco delle pubblicazioni e dei prodotti della ricerca per l'anno 2023 (Mostra tutto | Nascondi tutto)
Tipologia Titolo Pubblicazione/Prodotto
Abstract in Atti di convegno
A computational framework for predictive digital twins of civil engineering structures (Mostra >>)
A computational framework for predictive digital twins of civil engineering structures (Mostra >>)
A computational framework for predictive digital twins of civil structures using neural networks and probabilistic graphical models (Mostra >>)
Digital Twins of Civil Structures Using Neural Networks and Probabilistic Graphical Models (Mostra >>)
Contributi su volumi (Capitolo o Saggio)
Damage identification using physics-based datasets: From convolutional to metric-informed damage-sensitive feature extractors (Mostra >>)
Contributo in Atti di convegno
A Deep Neural Network, Multi-fidelity Surrogate Model Approach for Bayesian Model Updating in SHM (Mostra >>)
A tissue-aware simulation framework for [18F]FLT spatiotemporal uptake in pancreatic ductal adenocarcinoma (Mostra >>)
Articoli su riviste
A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Mostra >>)
A multi-fidelity surrogate model for structural health monitoring exploiting model order reduction and artificial neural networks (Mostra >>)
Approximation bounds for convolutional neural networks in operator learning (Mostra >>)
Deep learning-based surrogate models for parametrized PDEs: Handling geometric variability through graph neural networks (Mostra >>)
Efficient and certified solution of parametrized one-way coupled problems through DEIM-based data projection across non-conforming interfaces (Mostra >>)
Learning high-order interactions for polygenic risk prediction (Mostra >>)
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models (Mostra >>)
Mesh-Informed Neural Networks for Operator Learning in Finite Element Spaces (Mostra >>)
Multi-fidelity surrogate modeling using long short-term memory networks (Mostra >>)
Projection-based reduced order models for parameterized nonlinear time-dependent problems arising in cardiac mechanics (Mostra >>)
Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches (Mostra >>)
Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions (Mostra >>)
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression (Mostra >>)


Elenco delle pubblicazioni e dei prodotti della ricerca per l'anno 2022 (Mostra tutto | Nascondi tutto)
Tipologia Titolo Pubblicazione/Prodotto
Abstract in Atti di convegno
Enabling supervised learning in structural health monitoring by simulating damaged structure responses through physics based models (Mostra >>)
Enhanced Bayesian model updating for structural health monitoring via deep learning (Mostra >>)
Contributi su volumi (Capitolo o Saggio)
A Self-adaptive Hybrid Model/data-Driven Approach to SHM Based on Model Order Reduction and Deep Learning (Mostra >>)
Combined Model Order Reduction Techniques and Artificial Neural Network for Data Assimilation and Damage Detection in Structures (Mostra >>)
Contributo in Atti di convegno
A Generative Adversarial Network Based Autoencoder for Structural Health Monitoring (Mostra >>)
A Multi-Fidelity Deep Neural Network Approach to Structural Health Monitoring (Mostra >>)
Deep learning-based reduced order models in cardiac electrophysiology (Mostra >>)
Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate (Mostra >>)
Indirect Optimal Control of Advection-Diffusion Fields through Distributed Robotic Swarms (Mostra >>)
Unscented Kalman Filter Empowered by Bayesian Model Evidence for System Identification in Structural Dynamics (Mostra >>)
Articoli su riviste
Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures (Mostra >>)
Deep-HyROMnet: A Deep Learning-Based Operator Approximation for Hyper-Reduction of Nonlinear Parametrized PDEs (Mostra >>)
Density Control of Large-Scale Particles Swarm Through PDE-Constrained Optimization (Mostra >>)
Fast active thermal cloaking through PDE-constrained optimization and reduced-order modelling (Mostra >>)
Multi-fidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities (Mostra >>)
POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition (Mostra >>)
Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition (Mostra >>)
SHM under varying environmental conditions: an approach based on model order reduction and deep learning (Mostra >>)
Slow Conduction Corridors and Pivot Sites Characterize the Electrical Remodeling in Atrial Fibrillation (Mostra >>)
Stabilized reduced-order models for unsteady incompressible flows in three-dimensional parametrized domains (Mostra >>)
Statistical closure modeling for reduced-order models of stationary systems by the ROMES method (Mostra >>)
Structural health monitoring of civil structures: A diagnostic framework powered by deep metric learning (Mostra >>)


Elenco delle pubblicazioni e dei prodotti della ricerca per l'anno 2021 (Mostra tutto | Nascondi tutto)
Tipologia Titolo Pubblicazione/Prodotto
Abstract in Rivista
PH-0656 Prediction of toxicity after prostate cancer RT: the value of a SNP-interaction polygenic risk score (Mostra >>)
Abstract in Atti di convegno
A deep learning approach to metric-based damage localization in structural health monitoring (Mostra >>)
An MCMC approach powered by a multi-fidelity deep neural network surrogate for damage localization in civil structures (Mostra >>)
Dealing with uncertainties in structural damage localization by reduced order modeling and deep learning-based classifiers (Mostra >>)
Uncertainty Quantification for Parameter dependent Partial Differential Equations using Deep Neural Networks (Mostra >>)
Contributi su volumi (Capitolo o Saggio)
Computational bottlenecks for PROMs: Precomputation and hyperreduction (Mostra >>)
Contributo in Atti di convegno
Parametric reduced order modelling and deep learning to accomplish pattern recognition and regression tasks (Mostra >>)
Monografie o trattati scientifici
Optimal Control of Partial Differential Equations. Analysis, Approximation, and Applications (Mostra >>)
Articoli su riviste
A Comprehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs (Mostra >>)
A Computational Study of the Electrophysiological Substrate in Patients Suffering From Atrial Fibrillation (Mostra >>)
An autoencoder-based deep learning approach for load identification in structural dynamics (Mostra >>)
Characterization of cardiac electrogram signals in atrial arrhythmias (Mostra >>)
Data integration for the numerical simulation of cardiac electrophysiology (Mostra >>)
Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity (Mostra >>)
Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning (Mostra >>)
Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks (Mostra >>)
Online structural health monitoring by model order reduction and deep learning algorithms (Mostra >>)
POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium (Mostra >>)
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models (Mostra >>)
SUIHTER : a new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy (Mostra >>)
manifesti v. 3.9.3 / 3.9.3
Area Servizi ICT
22/04/2025