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Andrea Manzoni

Politecnico di Milano

Italy

Research Interests

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Professors in Italy Professors in Fluid Mechanics Fluid Mechanics professors in Italy

Articles (20)

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2026

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2025

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2023

Collaborators (10)

Luca Rosafalco

Politecnico di Milano

ITALY

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Alberto Corigliano

Docenti di ruolo di Ia fascia

Politecnico di Milano

ITALY

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Paolo Zunino

Full Professor

Politecnico di Milano

ITALY

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Mengwu Guo

Associate Professor

Lund University

SWEDEN

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Steve Brunton

Director

University of Washington

UNITED STATES

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Stefania Fresca

Assistant Professor

University of Washington

UNITED STATES

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Jörg Fehr

Professor

University of Stuttgart

GERMANY

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Francesca Ieva

Associate Professor

Politecnico di Milano

ITALY

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Michela Carlotta Massi

Staff Scientist

Human Technopole

ITALY

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Giovanni Pezzulo

Istituto di Scienze e Tecnologie della Cognizione

ITALY

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Andrea Manzoni

Research Interests

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Articles (20)

Multi-fidelity delayed acceptance: Hierarchical MCMC sampling for Bayesian inverse problems combining multiple solvers through deep neural networks

Active digital twins via active inference

Progressive multi-fidelity learning with neural networks for physical system predictions

VENI, VINDy, VICI: A generative reduced-order modeling framework with uncertainty quantification

Adaptive digital twins for predictive decision-making: online Bayesian learning of transition dynamics

Handling geometrical variability in nonlinear reduced order modeling through Continuous Geometry-Aware DL-ROMs

On latent dynamics learning in nonlinear reduced order modeling

Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models

Multi-fidelity reduced-order surrogate modelling

PTPI-DL-ROMs: Pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs

EKF–SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics

Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition

Efficient approximation of cardiac mechanics through reduced‐order modeling with deep learning‐based operator approximation

Learning high-order interactions for polygenic risk prediction

Approximation bounds for convolutional neural networks in operator learning

Mesh-Informed Neural Networks for Operator Learning in Finite Element Spaces

Deep learning-based surrogate models for parametrized PDEs: Handling geometric variability through graph neural networks

Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions

Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches

A multi-fidelity surrogate model for structural health monitoring exploiting model order reduction and artificial neural networks

Collaborators (10)

Luca Rosafalco

Alberto Corigliano

Paolo Zunino

Mengwu Guo

Steve Brunton

Stefania Fresca

Jörg Fehr

Francesca Ieva

Michela Carlotta Massi

Giovanni Pezzulo

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