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 1320 prodotti
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62/2025 - 10/10/2025
Rigamonti, V.; Torri, V.; Morris, S. K.; Ieva, F.; Giaquinto, C.; Donà, D.; Di Chiara, C.; Cantarutti, A.; CARICE study group
Real-World Effectiveness of Influenza Vaccination in Preventing Influenza and Influenza-Like Illness in Children | Abstract | | Background and objectives: Quadrivalent live attenuated influenza vaccines (LAIV-4) offer an alternative to inactivated influenza vaccines (IIV) for children aged 2–17 years, but data on their comparative effectiveness are limited. This study assessed vaccination rates and real-world effectiveness of LAIV-4 and IIV in preventing influenza and influenza-like illness (ILI) in Italian children during the 2022–2023 and 2023–2024 seasons.
Methods: We conducted a population-based cohort study of children aged 2–14 years from September 2022 to April 2024, using data from Pedianet, a pediatric primary care database of anonymized records from family pediatricians. Children vaccinated with LAIV-4 or IIV were compared to unvaccinated children. The primary outcome was any first influenza or ILI episode. Monthly vaccination incidence rates per 1000 person-months were calculated for each vaccine type. Hazard ratios (HRs) and their 95 % confidence intervals (CIs) for vaccine effectiveness (VE) were estimated using adjusted mixed-effects Cox models.
Results: A total of 65,545 (472,173 person-months) and 72,377 (527,348 person-months) children were included for the 2022–2023 and 2023–2024 seasons, respectively. Vaccination rates were 12.71 and 12.85 per 1000 person-months, respectively. Compared to unvaccinated children, LAIV-4 had an overall effectiveness of 43% (95% CI, 32%–53%), while IIV effectiveness was 54% (95% CI, 46%–61%). In 2022–2023, LAIV-4 (38% [95% CI, 12%–56%]) and IIV (49% [95% CI, 37%–58%]) had comparable effectiveness. In 2023–2024, LAIV-4 (40 % [95% CI, 25%–52%]) was slightly less effective than IIV (58% [95% CI, 44%–68%])(p = 0.048).
Conclusions: An overall moderate, comparable effectiveness of LAIV-4 and IIV in preventing influenza/ILI among Italian children was observed.
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61/2025 - 07/10/2025
Coclite, A; Montanelli Eccher, R.; Possenti, L.; Vitullo, P.; Zunino, P.
Mathematical modeling and sensitivity analysis of hypoxia-activated drugs | Abstract | | Hypoxia-activated prodrugs offer a promising strategy for targeting oxygen-deficient regions in solid tumors, which are often resistant to conventional therapies. However, modeling their behavior is challenging because of the complex interplay between oxygen availability, drug activation, and cell survival. In this work, we develop a multiscale and mixed-dimensional model that couples spatially resolved drug and oxygen transport with pharmacokinetics and pharmacodynamics to simulate the cellular response. The model integrates blood flow, oxygen diffusion, and consumption, drug delivery, and metabolism. To reduce computational cost, we mitigate the global nonlinearity by coupling the multiscale and mixed-dimensional models one-way with a reduced 0D model for drug metabolization. The global sensitivity analysis is then used to identify key parameters influencing drug activation and therapeutic outcome. This approach enables efficient simulation and supports the design of optimized therapies targeting hypoxia. |
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60/2025 - 02/10/2025
Antonietti, P. F.; Caldana, M.; Gentile, L.; Verani M.
Deep Learning Accelerated Algebraic Multigrid Methods for Polytopal Discretizations of Second-Order Differential Problems | Abstract | | Algebraic Multigrid (AMG) methods are state-of-the-art algebraic solvers for partial differential equations. Still, their efficiency depends heavily on the choice of suitable parameters and/or ingredients. Paradigmatic examples include the so-called strong threshold parameter, which controls the algebraic coarse-grid hierarchy, as well as the smoother, i.e., the relaxation methods used on the fine grid to damp out high-frequency errors. In AMG, since the coarse grids are constructed algebraically (without geometric intuition), the smoother's performance is even more critical.
For the linear systems stemming from polytopal discretizations, such as Polytopal Discontinuous Galerkin (PolyDG) and Virtual Element Methods (VEM), AMG sensitivity to such choices is even more critical due to the significant variability of the underlying meshes, which results in algebraic systems with different sparsity patterns.
We propose a novel deep learning approach that automatically tunes the strong threshold parameter, as well as the smoother choice in AMG solvers, for linear systems of equations arising from polytopal discretizations, thereby maximizing AMG performance. We interpret the sparse matrix resulting from polytopal discretization as a grayscale image, and by applying pooling, our neural network extracts compact features that preserve the necessary information at a low computational cost.
We test various differential problems in both two- and three-dimensional settings, with heterogeneous coefficients and polygonal/polyhedral meshes, and demonstrate that the proposed approach generalizes well. In practice, we demonstrate that we can reduce AMG solver time by up to 27% with minimal changes to existing PolyDG and VEM codes. |
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59/2025 - 01/10/2025
Wolf, F.; Botteghi, N.; Fasel, U.; Manzoni, A.
Interpretable and efficient data-driven discovery and control of distributed systems | Abstract | | Effectively controlling systems governed by Partial Differential Equations (PDEs) is crucial in several fields of Applied Sciences and Engineering. These systems usually yield significant challenges to conventional control schemes due to their nonlinear dynamics, partial observability, high-dimensionality once discretized, distributed nature, and the requirement for low-latency feedback control. Reinforcement Learning (RL), particularly Deep RL (DRL), has recently emerged as a promising control paradigm for such systems, demonstrating exceptional capabilities in managing high-dimensional, nonlinear dynamics. However, DRL faces challenges including sample inefficiency, robustness issues, and an overall lack of interpretability. To address these issues, we propose a data-efficient, interpretable, and scalable Dyna-style Model-based RL framework for PDE control, combining the Sparse Identification of Nonlinear Dynamics with Control (SINDy-C) algorithm and an Autoencoder (AE) framework for the sake of dimensionality reduction of PDE states and actions. This novel approach enables fast rollouts, reducing the need for extensive environment interactions, and provides an interpretable latent space representation of the PDE forward dynamics. To address these challenges, we propose a data-efficient, interpretable, and scalable Dyna-style Model-based Reinforcement Learning framework specifically tailored for PDE control. Our approach integrates Sparse Identification of Nonlinear Dynamics with Control within an Autoencoder-based dimensionality reduction scheme for PDE states and actions (AE+SINDy-C). This combination enables fast rollouts with significantly fewer environment interactions, while providing an interpretable latent space representation of the PDE dynamics, facilitating insight into the control process. We validate our method on two PDE problems describing fluid flows - namely, the 1D Burgers equation and 2D Navier-Stokes equations - comparing it against a model-free baseline. Our extensive analysis highlights improved sample efficiency, stability, and interpretability in controlling complex PDE systems. |
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58/2025 - 23/09/2025
Pivato, C.A.; Cozzi, O.; Fontana, N.; Ieva, F., et al.
Clinical outcomes of percutaneous coronary interventions after transcatheter aortic valve replacement | Abstract | | The number of patients undergoing percutaneous coronary interventions (PCI) after transcatheter aortic valve replacement (TAVR) is expected to increase, but their prognosis remains poorly understood.
Consecutive PCI patients with prior TAVR were compared to patients without prior TAVR between 2008 and 2023. The Kaplan–Meier method was used to estimate the 1-year incidence of major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death or myocardial infarction. An entropy balance approach was implemented to adjust for imbalances in patient and procedural characteristics. Adjusted hazard ratios (HRs) were estimated using weighted Cox regression models. Comparing 420 PCI patients with prior TAVR (mean age 80.8 years, 37.1% women) to 1197 without (mean age 70.4 years, 24.6% women), 1-year MACE was higher in the prior TAVR group (8.7 vs. 3.7%; unadjusted HR 2.35, 95% CI 1.49–3.69; P < 0.001). After adjustment for clinical and procedural characteristics, prior TAVR remained associated with an increased risk of MACE (adjusted HR 2.36, 95% CI 1.08–5.16; P = 0.032). This was primarily driven by higher cardiovascular death (adjusted HR 3.12, 95% CI 1.10–8.79, P = 0.032), while the association with myocardial infarction was attenuated post-adjustment and no longer statistically significant.
Patients undergoing PCI after TAVR experience a higher incidence of MACE compared to those undergoing PCI without prior TAVR, underscoring the importance of accurate patient selection before performing PCI in patients with chronic coronary syndrome and history of TAVR. |
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57/2025 - 17/09/2025
Guagliardi, O.; Masci, C,; Breschi, V; Paganoni A, ; Tanelli, M.
A Novel DNA-Inspired Framework to Study University Dropout: Insights from Politecnico di Milano | Abstract | | This study presents Dropout-DNA, a novel data-driven tool designed to assess university dropout risk by profiling students through a combination of early indicators and academic progress. The approach emphasizes the need for context-aware and interpretable models in predicting student dropout, offering a significant advancement in the field of student retention analytics. Results show that while early indicators are valuable, incorporating academic performance significantly enhances predictive accuracy. The model, although generalizable across engineering courses, performs best when tailored to the specific degree program it was trained on. This finding underlines the importance of adapting predictive tools to the unique characteristics and dropout patterns of individual study programs. The practical implications are considerable: by identifying at-risk students early, institutions can implement targeted and personalized interventions, improving the effectiveness of student support services. The Dropout-DNA’s quantifiable representation of risk allows for more strategic policy-making at the institutional level. Looking ahead, future research will focus on the temporal evolution of dropout risk profiles, enabling dynamic, time-sensitive monitoring and intervention throughout the academic journey. |
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56/2025 - 14/09/2025
Tonini, A.; Bui-Thanh, T.; Regazzoni, F.; Dede', L; Quarteroni, A.
Improvements on uncertainty quantification with variational autoencoders | Abstract | | Inverse problems aim to determine model parameters of a mathematical problem from given observational data. Neural networks can provide an efficient tool to solve these problems. In the context of Bayesian inverse problems, Uncertainty Quantification Variational AutoEncoders (UQ-VAE), a class of neural networks, approximate the posterior distribution mean and covariance of model parameters. This allows for both the estimation of the parameters and their uncertainty in relation to the observational data. In this work, we propose a novel loss function for training UQ-VAEs, which includes, among other modifications, the removal of a sample mean term from an already existing one. This modification improves the accuracy of UQ-VAEs, as the original theoretical result relies on the convergence of the sample mean to the expected value (a condition that, in high dimensional parameter spaces, requires a prohibitively large number of samples due to the curse of dimensionality). Avoiding the computation of the sample mean significantly reduces the training time in high dimensional parameter spaces compared to previous literature results. Under this new formulation, we establish a new theoretical result for the approximation of the posterior mean and covariance for general mathematical problems. We validate the effectiveness of UQ-VAEs through three benchmark numerical tests: a Poisson inverse problem, a non affine inverse problem and a 0D cardiocirculatory model, under the two clinical scenarios of systemic hypertension and ventricular septal defect. For the latter case, we perform forward uncertainty quantification. |
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54/2025 - 08/09/2025
Tomasetto, M.; Williams, J.P.; Braghin, F.; Manzoni, A.; Kutz, J.N.
Reduced order modeling with shallow recurrent decoder networks | Abstract | | Reduced Order Modeling is of paramount importance for efficiently inferring high-dimensional spatio-temporal fields in parametric contexts, enabling computationally tractable parametric analyses, uncertainty quantification and control. However, conventional dimensionality reduction techniques are typically limited to known and constant parameters, inefficient for nonlinear and chaotic dynamics, and uninformed to the actual system behavior. In this work, we propose sensor-driven SHallow REcurrent Decoder networks for Reduced Order Modeling (SHRED-ROM). Specifically, we consider the composition of a long short-term memory network, which encodes the temporal dynamics of limited sensor data in multiple scenarios, and a shallow decoder, which reconstructs the corresponding high-dimensional states. SHRED-ROM is a robust decoding-only strategy that circumvents the numerically unstable approximation of an inverse which is required by encoding-decoding schemes. To enhance computational efficiency and memory usage, the full-order state snapshots are reduced by, e.g., proper orthogonal decomposition, allowing for compressive training of the networks with minimal hyperparameter tuning. Through applications on chaotic and nonlinear fluid dynamics, we show that SHRED ROM (i) accurately reconstructs the state dynamics for new parameter values starting from limited fixed or mobile sensors, independently on sensor placement, (ii) can cope with both physical, geometrical and time dependent parametric dependencies, while being agnostic to their actual values, (iii) can accurately estimate unknown parameters, and (iv) can deal with different data sources, such as high-fidelity simulations, coupled fields and videos. |
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