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Information on didactic, research and institutional assignments on this page are certified by the University; more information, prepared by the lecturer, are available on the personal web page and in the curriculum vitae indicated on this webpage.
Information
LecturerManzoni Andrea
QualificationAssociate professor full time
Belonging DepartmentDipartimento di Matematica
Scientific-Disciplinary SectorMATH-05/A - Numerical Analysis
Curriculum VitaeDownload CV (328.24Kb - 08/01/2025)
OrcIDhttps://orcid.org/0000-0001-8277-2802

Contacts
Office hours
DepartmentFloorOfficeDayTimetableTelephoneFaxNotes
Matematica6---ThursdayFrom 11:00
To 13:00
4638---Contattare il docente per e-mail per fissare un appuntamento
E-mailandrea1.manzoni@polimi.it
Personal website---

Data source: RE.PUBLIC@POLIMI - Research Publications at Politecnico di Milano

List of publications and reserach products for the year 2025 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Abstract in Atti di convegno
Online learning of time-varying dynamic systems with EKF-SINDy (Show >>)
Predictive Digital Twins for Congestive Heart Failure Patients (Show >>)
Predictive digital twins for health monitoring: From structural safety to personalized medicine (Show >>)
Journal Articles
On latent dynamics learning in nonlinear reduced order modeling (Show >>)
Online learning in bifurcating dynamic systems via SINDy and Kalman filtering (Show >>)


List of publications and reserach products for the year 2024 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Abstract in Atti di convegno
Predictive Digital Twins for Optimized Management and Maintenance of Civil Structures (Show >>)
Predictive digital twins of engineering structures using neural networks and probabilistic graphical models (Show >>)
Poster
A predictive Digital Twin framework for risk assessment in congestive heart failure (Show >>)
Contributions on scientific books
Deep Learning-Based Reduced Order Models for Cardiac Electrophysiology (Show >>)
Conference proceedings
SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study (Show >>)
Journal Articles
A Reduced Order Model for Domain Decompositions with Non-conforming Interfaces (Show >>)
A digital twin framework for civil engineering structures (Show >>)
A non-conforming-in-space numerical framework for realistic cardiac electrophysiological outputs (Show >>)
An optimal control strategy to design passive thermal cloaks of arbitrary shape (Show >>)
EKF–SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics (Show >>)
Efficient approximation of cardiac mechanics through reduced‐order modeling with deep learning‐based operator approximation (Show >>)
Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition (Show >>)
Multi-fidelity reduced-order surrogate modelling (Show >>)
Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers (Show >>)
Nonlinear model order reduction for problems with microstructure using mesh informed neural networks (Show >>)
On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields (Show >>)
PTPI-DL-ROMs: Pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Show >>)
Unveiling the biological side of PET-derived biomarkers: a simulation-based approach applied to PDAC assessment (Show >>)


List of publications and reserach products for the year 2023 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Abstract in Atti di convegno
A computational framework for predictive digital twins of civil engineering structures (Show >>)
A computational framework for predictive digital twins of civil engineering structures (Show >>)
A computational framework for predictive digital twins of civil structures using neural networks and probabilistic graphical models (Show >>)
Digital Twins of Civil Structures Using Neural Networks and Probabilistic Graphical Models (Show >>)
Contributions on scientific books
Damage identification using physics-based datasets: From convolutional to metric-informed damage-sensitive feature extractors (Show >>)
Conference proceedings
A Deep Neural Network, Multi-fidelity Surrogate Model Approach for Bayesian Model Updating in SHM (Show >>)
A tissue-aware simulation framework for [18F]FLT spatiotemporal uptake in pancreatic ductal adenocarcinoma (Show >>)
Journal Articles
A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Show >>)
A multi-fidelity surrogate model for structural health monitoring exploiting model order reduction and artificial neural networks (Show >>)
Approximation bounds for convolutional neural networks in operator learning (Show >>)
Deep learning-based surrogate models for parametrized PDEs: Handling geometric variability through graph neural networks (Show >>)
Efficient and certified solution of parametrized one-way coupled problems through DEIM-based data projection across non-conforming interfaces (Show >>)
Learning high-order interactions for polygenic risk prediction (Show >>)
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models (Show >>)
Mesh-Informed Neural Networks for Operator Learning in Finite Element Spaces (Show >>)
Multi-fidelity surrogate modeling using long short-term memory networks (Show >>)
Projection-based reduced order models for parameterized nonlinear time-dependent problems arising in cardiac mechanics (Show >>)
Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches (Show >>)
Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions (Show >>)
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression (Show >>)


List of publications and reserach products for the year 2022 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Abstract in Atti di convegno
Enabling supervised learning in structural health monitoring by simulating damaged structure responses through physics based models (Show >>)
Enhanced Bayesian model updating for structural health monitoring via deep learning (Show >>)
Contributions on scientific books
A Self-adaptive Hybrid Model/data-Driven Approach to SHM Based on Model Order Reduction and Deep Learning (Show >>)
Combined Model Order Reduction Techniques and Artificial Neural Network for Data Assimilation and Damage Detection in Structures (Show >>)
Conference proceedings
A Generative Adversarial Network Based Autoencoder for Structural Health Monitoring (Show >>)
A Multi-Fidelity Deep Neural Network Approach to Structural Health Monitoring (Show >>)
Deep learning-based reduced order models in cardiac electrophysiology (Show >>)
Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate (Show >>)
Indirect Optimal Control of Advection-Diffusion Fields through Distributed Robotic Swarms (Show >>)
Unscented Kalman Filter Empowered by Bayesian Model Evidence for System Identification in Structural Dynamics (Show >>)
Journal Articles
Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures (Show >>)
Deep-HyROMnet: A Deep Learning-Based Operator Approximation for Hyper-Reduction of Nonlinear Parametrized PDEs (Show >>)
Density Control of Large-Scale Particles Swarm Through PDE-Constrained Optimization (Show >>)
Fast active thermal cloaking through PDE-constrained optimization and reduced-order modelling (Show >>)
Multi-fidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities (Show >>)
POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition (Show >>)
Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition (Show >>)
SHM under varying environmental conditions: an approach based on model order reduction and deep learning (Show >>)
Slow Conduction Corridors and Pivot Sites Characterize the Electrical Remodeling in Atrial Fibrillation (Show >>)
Stabilized reduced-order models for unsteady incompressible flows in three-dimensional parametrized domains (Show >>)
Statistical closure modeling for reduced-order models of stationary systems by the ROMES method (Show >>)
Structural health monitoring of civil structures: A diagnostic framework powered by deep metric learning (Show >>)


List of publications and reserach products for the year 2021 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Abstract in Rivista
PH-0656 Prediction of toxicity after prostate cancer RT: the value of a SNP-interaction polygenic risk score (Show >>)
Abstract in Atti di convegno
A deep learning approach to metric-based damage localization in structural health monitoring (Show >>)
An MCMC approach powered by a multi-fidelity deep neural network surrogate for damage localization in civil structures (Show >>)
Dealing with uncertainties in structural damage localization by reduced order modeling and deep learning-based classifiers (Show >>)
Uncertainty Quantification for Parameter dependent Partial Differential Equations using Deep Neural Networks (Show >>)
Contributions on scientific books
Computational bottlenecks for PROMs: Precomputation and hyperreduction (Show >>)
Conference proceedings
Parametric reduced order modelling and deep learning to accomplish pattern recognition and regression tasks (Show >>)
Scientific Books
Optimal Control of Partial Differential Equations. Analysis, Approximation, and Applications (Show >>)
Journal Articles
A Comprehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs (Show >>)
A Computational Study of the Electrophysiological Substrate in Patients Suffering From Atrial Fibrillation (Show >>)
An autoencoder-based deep learning approach for load identification in structural dynamics (Show >>)
Characterization of cardiac electrogram signals in atrial arrhythmias (Show >>)
Data integration for the numerical simulation of cardiac electrophysiology (Show >>)
Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity (Show >>)
Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning (Show >>)
Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks (Show >>)
Online structural health monitoring by model order reduction and deep learning algorithms (Show >>)
POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium (Show >>)
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models (Show >>)
SUIHTER : a new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy (Show >>)
manifesti v. 3.9.3 / 3.9.3
Area Servizi ICT
27/04/2025