Quaderni MOX
Pubblicazioni
del Laboratorio di Modellistica e Calcolo Scientifico MOX. I lavori riguardano prevalentemente il campo dell'analisi numerica, della statistica e della modellistica matematica applicata a problemi di interesse ingegneristico. Il sito del Laboratorio MOX è raggiungibile
all'indirizzo mox.polimi.it
Trovati 1237 prodotti
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14/2025 - 28/02/2025
Nicolussi, F.; Masci, C.
Stratified Multilevel Graphical Models: Examining Gender Dynamics in Education | Abstract | | This study proposes a methodological approach to investigate gender disparities in education, particularly focusing on the schooling phase and its influence on career trajectories. The research applies multilevel linear models to analyse student performance concerning various factors, with a specific emphasis on gender-specific outcomes.
The study aims to identify and test context-specific independencies that may impact educational disparities between genders. The methodology includes the introduction of supplementary parameters in multilevel models to capture and examine these independencies. Furthermore, the research proposes encoding these novel relationships in graphical models, specifically stratified chain graph models, to visualize and generalize the complex dependencies among covariates, random effects, and gender influences on educational outcomes. |
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12/2025 - 26/02/2025
Alessandro Andrea Zecchi, Claudio Sanavio, Simona Perotto e Sauro Succi
Improved amplitude amplification strategies for the quantum simulation of classical transport problems | Abstract | | The quantum simulation of classical fluids often involves the use of probabilistic algorithms that encode the result of the dynamics in the form of the amplitude of the selected quantum state. In most cases, however, the amplitude probability is too low to allow an efficient use of these algorithms, thereby hindering the practical viability of the quantum simulation. The oblivious amplitude amplification algorithm is often presented as a solution to this problem, but to no avail for most classical problems, since its applicability is limited to unitary dynamics. In this paper, we show analytically that oblivious amplitude amplification when applied to non-unitary dynamics leads to a distortion of the quantum state and to an accompanying error in the quantum update. We provide an analytical upper bound of such error as a function of the degree of non-unitarity of the dynamics and we test it against a quantum simulation of an advection-diffusion-reaction equation, a transport problem of major relevance in science and engineering. Finally, we also propose an amplification strategy that helps mitigate the distortion error, while still securing an enhanced success probability. |
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11/2025 - 19/02/2025
Tonini, A.; Dede', L.
Enhanched uncertainty quantification variational autoencoders for the solution of Bayesian inverse problems | Abstract | | Among other uses, neural networks are a powerful tool for solving deterministic and Bayesian inverse problems in
real-time. In the Bayesian framework, variational autoencoders, a specialized type of neural network, enable the estimation of model
parameters and their distribution based on observational data allowing to perform real-time inverse uncertainty quantification. In this
work, we build upon existing research [Goh, H. et al., Proceedings of Machine Learning Research, 2022] by proposing a novel loss
function to train variational autoencoders for Bayesian inverse problems. When the forward map is affine, we provide a theoretical
proof of the convergence of the latent states of variational autoencoders to the posterior distribution of the model parameters. We
validate this theoretical result through numerical tests and we compare the proposed variational autoencoder with the existing one in the literature. Finally, we test the proposed variational autoencoder on the Laplace equation. |
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10/2025 - 13/02/2025
Botti, M.; Mascotto, L.; Vacca, G.; Visinoni, M.
Stability and interpolation estimates of Hellinger-Reissner virtual element spaces | Abstract | | We prove stability and interpolation estimates for Hellinger-Reissner virtual elements; the constants appearing in such estimates only depend on the aspect ratio of the polytope under consideration and the degree of accuracy of the scheme. We further investigate numerically the behaviour of the constants appearing in the stability estimates on sequences of badly-shaped polytopes and for increasing degree of accuracy. |
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09/2025 - 03/02/2025
Quarteroni, A.; Gervasio, P.; Regazzoni, F.
Combining physics-based and data-driven models: advancing the frontiers of research with Scientific Machine Learning | Abstract | | Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at hand, subsequent mathematical formulation, and numerical approximation. Data-driven models instead aim to extract relations between input and output data without arguing any causality principle underlining the available data distribution. In recent years, data-driven models have been rapidly developed and popularized. Such a diffusion has been triggered by a huge availability of data (the so-called big data), an increasingly cheap computing power, and the development of powerful machine learning algorithms. SciML leverages the physical awareness of physics-based models and, at the same time, the efficiency of data-driven algorithms. With SciML, we can inject physics and mathematical knowledge into machine learning algorithms. Yet, we can rely on data-driven algorithms’ capability to discover complex and non-linear patterns from data and improve the descriptive capacity of physics-based models. After recalling the mathematical foundations of digital modelling and machine learning algorithms, and presenting the most popular machine learning architectures, we discuss the great potential of a broad variety of SciML strategies in solving complex problems governed by partial differential equations. Finally, we illustrate the successful application of SciML to the simulation of the human cardiac function, a field of significant socio-economic importance that poses numerous challenges on both the mathematical and computational fronts. The corresponding mathematical model is a complex system of non-linear ordinary and partial differential equations describing the electromechanics, valve dynamics, blood circulation, perfusion in the coronary tree, and torso potential. Despite the robustness and accuracy of physics-based models, certain aspects, such as unveiling constitutive laws for cardiac cells and myocardial material properties, as well as devising efficient reduced order models to dominate the extraordinary computational complexity, have been successfully tackled by leveraging data-driven models. |
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08/2025 - 31/01/2025
Botti, M.; Fumagalli, I.; Mazzieri, I.
Polytopal discontinuous Galerkin methods for low-frequency poroelasticity coupled to unsteady Stokes flow | Abstract | | We focus on the numerical analysis of a polygonal discontinuous Galerkin scheme for the simulation of the exchange of fluid between a deformable saturated poroelastic structure and an adjacent free-flow channel. We specifically address wave phenomena described by the low-frequency Biot model in the poroelastic region and unsteady Stokes flow in the open channel, possibly an isolated cavity or a connected fracture system. The coupling at the interface between the two regions is realized by means of transmission conditions expressing conservation laws.
The spatial discretization hinges on the weak form of the two-displacement poroelasticity system and a stress formulation of the Stokes equation with weakly imposed symmetry. We present a complete stability analysis for the proposed semi-discrete formulation and derive a-priori hp-error estimates. |
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07/2025 - 30/01/2025
Patanè, G.; Nicolussi, F.; Krauth, A.; Gauglitz, G.; Colosimo, B. M.; Dede', L.; Menafoglio, A.
Functional-Ordinal Canonical Correlation Analysis With Application to Data from Optical Sensors | Abstract | | We address the problem of predicting a target ordinal variable based on observable features consisting of functional profiles. This problem is crucial, especially in decision-making driven by sensor systems, when the goal is to assess a ordinal variable such as the degree of deterioration, quality level, or risk stage of a process, starting from functional data observed via sensors. We purposely introduce a novel approach called functional-ordinal Canonical Correlation Analysis (foCCA), which is based on a functional data analysis approach. FoCCA allows for dimensionality reduction of observable features while maximizing their ability to differentiate between consecutive levels of an ordinal target variable. Unlike existing methods for supervised learning from functional data, foCCA fully incorporates the ordinal nature of the target variable. This enables the model to capture and represent the relative dissimilarities between consecutive levels of the ordinal target, while also explaining these differences through the functional features. Extensive simulations demonstrate that foCCA outperforms current state-of-the-art methods in terms of prediction accuracy in the reduced feature space. A case study involving the prediction of antigen concentration levels from optical biosensor signals further confirms the superior performance of foCCA, showcasing both improved predictive power and enhanced interpretability compared to competing approaches. |
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06/2025 - 17/01/2025
Torzoni, M.; Manzoni, A.; Mariani, A.
Enhancing Bayesian model updating in structural health monitoring via learnable mappings | Abstract | | In the context of structural health monitoring (SHM), the selection and extraction of damage sensitive features from raw sensor recordings represent a critical step towards solving the inverse problem underlying the structural health identification. This work introduces a new way to enhance stochastic approaches to SHM through the use of deep neural networks. A learnable feature extractor and a feature-oriented surrogate model are synergistically exploited to evaluate a likelihood function within a Markov chain Monte Carlo sampling algorithm. The feature extractor undergoes a supervised pairwise training to map sensor recordings onto a low-dimensional metric space, which encapsulates the sensitivity to structural health parameters. The surrogate model maps the structural health parameters onto their feature description. The procedure enables the updating of beliefs about structural health parameters, effectively replacing the need for a computationally expensive numerical (finite element) model. A preliminary offline phase involves the generation of a labeled dataset to train both the feature extractor and the surrogate model. Within a simulation-based SHM framework, training vibration responses are cost-effectively generated by means of a multi-fidelity surrogate modeling strategy to approximate sensor recordings under varying damage and operational conditions. The multi-fidelity surrogate exploits model order reduction and artificial neural networks to speed up the data generation phase while ensuring the damage-sensitivity of the approximated signals. The proposed strategy is assessed through three synthetic case studies, demonstrating remarkable results in terms of accuracy of the estimated quantities and computational efficiency. |
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