Andrea Manzoni
Polytechnic University of Milan
ParametrizationAlgorithmParametric statisticsBasis (linear algebra)Mathematical optimizationMathematical analysisInverse problemFinite element methodArtificial intelligenceNonlinear systemProjection (linear algebra)Model order reductionApplied mathematicsShape optimizationMathematicsComputer scienceBoundary (topology)DiscretizationOptimal controlPartial differential equationReduction (complexity)
121Publications
22H-index
2,028Citations
Publications 112
Newest
#1Luca Rosafalco (Polytechnic University of Milan)H-Index: 3
#2Andrea Manzoni (Polytechnic University of Milan)H-Index: 22
Last. Alberto Corigliano (Polytechnic University of Milan)H-Index: 33
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In civil engineering, different machine learning algorithms have been adopted to process the huge amount of data continuously acquired through sensor networks and solve inverse problems. Challenging issues linked to structural health monitoring or load identification are currently related to big data, consisting of structural vibration recordings shaped as a multivariate time series. Any algorithm should therefore allow an effective dimensionality reduction, retaining the informative content of ...
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Simulating fluid flows in different virtual scenarios is of key importance in engineering applications. However, high-fidelity, full-order models relying, e.g., on the finite element method, are unaffordable whenever fluid flows must be simulated in almost real-time. Reduced order models (ROMs) relying, e.g., on proper orthogonal decomposition (POD) provide reliable approximations to parameter-dependent fluid dynamics problems in rapid times. However, they might require expensive hyper-reduction...
#1Carlo Sinigaglia (Polytechnic University of Milan)
#2Andrea Manzoni (Polytechnic University of Milan)H-Index: 22
Last. Francesco Braghin (Polytechnic University of Milan)H-Index: 23
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We describe in this paper an optimal control strategy for shaping a large-scale swarm of particles using boundary global actuation. This problem arises as a key challenge in many swarm robotics applications, especially when the robots are passive particles that need to be guided by external control fields. The system is large-scale and underactuated, making the control strategy at the microscopic particle level infeasible. We consider the Kolmogorov forward equation associated to the stochastic ...
#1Matteo Salvador (Polytechnic University of Milan)H-Index: 3
#2Luca Dedè (Polytechnic University of Milan)H-Index: 21
Last. Andrea Manzoni (Polytechnic University of Milan)H-Index: 22
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We propose a nonlinear reduced basis method for the efficient approximation of parametrized partial differential equations (PDEs), exploiting kernel proper orthogonal decomposition (KPOD) for the generation of a reduced-order space and neural networks for the evaluation of the reduced-order approximation. In particular, we use KPOD in place of the more classical POD, on a set of high-fidelity solutions of the problem at hand to extract a reduced basis. This method provides a more accurate approx...
#1Luca RosafalcoH-Index: 3
#2Matteo TorzoniH-Index: 1
Last. Alberto CoriglianoH-Index: 33
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Within a structural health monitoring (SHM) framework, we propose a simulation-based classification strategy to move towards online damage localization. The procedure combines parametric Model Order Reduction (MOR) techniques and Fully Convolutional Networks (FCNs) to analyze raw vibration measurements recorded on the monitored structure. First, a dataset of possible structural responses under varying operational conditions is built through a physics-based model, allowing for a finite set of pre...
1 Citations
#2Andrea ManzoniH-Index: 22
Last. Paolo ZuninoH-Index: 28
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Within the framework of parameter dependent PDEs, we develop a constructive approach based on Deep Neural Networks for the efficient approximation of the parameter-to-solution map. The research is motivated by the limitations and drawbacks of state-of-the-art algorithms, such as the Reduced Basis method, when addressing problems that show a slow decay in the Kolmogorov n-width. Our work is based on the use of deep autoencoders, which we employ for encoding and decoding a high fidelity approximat...
#1Mengwu GuoH-Index: 5
#2Andrea ManzoniH-Index: 22
Last. Jan S. HesthavenH-Index: 62
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Highly accurate numerical or physical experiments are often time-consuming or expensive to obtain. When time or budget restrictions prohibit the generation of additional data, the amount of available samples may be too limited to provide satisfactory model results. Multi-fidelity methods deal with such problems by incorporating information from other sources, which are ideally well-correlated with the high-fidelity data, but can be obtained at a lower cost. By leveraging correlations between dif...
#1Michela Carlotta Massi (Polytechnic University of Milan)H-Index: 2
Last. Paolo ZuninoH-Index: 28
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Logistic Regression (LR) is a widely used statistical method in empirical binary classification studies. However, real-life scenarios oftentimes share complexities that prevent from the use of the as-is LR model, and instead highlight the need to include high-order interactions to capture data variability. This becomes even more challenging because of: (i) datasets growing wider, with more and more variables; (ii) studies being typically conducted in strongly imbalanced settings; (iii) samples g...
#1Stefano Pagani (Polytechnic University of Milan)H-Index: 5
#2Luca Dedè (Polytechnic University of Milan)H-Index: 21
Last. Alfio Quarteroni (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 83
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The increasing availability of extensive and accurate clinical data is rapidly shaping cardiovascular care by improving the understanding of physiological and pathological mechanisms of the cardiovascular system and opening new frontiers in designing therapies and interventions. In this direction, mathematical and numerical models provide a complementary relevant tool, able not only to reproduce patient-specific clinical indicators but also to predict and explore unseen scenarios. With this goal...
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#1Stefano Pagani (Polytechnic University of Milan)H-Index: 5
#2Andrea Manzoni (Polytechnic University of Milan)H-Index: 22
We present a new, computationally efficient framework to perform forward uncertainty quantification (UQ) in cardiac electrophysiology. We consider the monodomain model to describe the electrical activity in the cardiac tissue, coupled with the Aliev-Panfilov model to characterize the ionic activity through the cell membrane. We address a complete forward UQ pipeline, including both: (i) a variance-based global sensitivity analysis for the selection of the most relevant input parameters, and (ii)...
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