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 1352 prodotti
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69/2026 - 04/09/2026
Zecchi, A. A.; Ferro, N.; Perotto, S.
Complexity-controlled QUBO formulation for topology optimization via anisotropic mesh adaptation | Abstract | | QUBO formulation provides a natural route to address topology optimization of continuum structures with classical heuristic, quantum-inspired, and quantum annealing solvers. Their practical applicability, however, is still limited by the number of binary variables that can be handled, especially on near-term quantum hardware. In this work, we tackle this bottleneck by proposing QUBOSIMPATY algorithm which combines a QUBO formulation of the SIMP minimum compliance problem with anisotropic recovery-based mesh adaptation, enriched with metric-based complexity control.
Specifically, we exploit anisotropic mesh adaptation to reduce the number of density degrees of freedom, while retaining directional resolution along the sharp material-void interfaces that characterize optimized layouts. A metric rescaling procedure is applied to prescribe the number of mesh vertices, and hence the number of binary variables, so as to comply with the severe size constraints imposed by quantum annealing hardware.
Numerical tests in two and three dimensions are carried out with classical QUBO heuristics, including simulated annealing and tabu search. Moreover, a proof-of-concept experiment is performed on quantum annealing hardware. The results show that the proposed strategy improves the suppression of intermediate densities while keeping the optimization problem within the admissible size range. |
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68/2026 - 04/09/2026
Patanè, G.; Greven, S.; Menafoglio, A.
Random mixtures in Bayes Hilbert spaces | Abstract | | We present a framework for the analysis and unmixing of random density mixtures in the Bayes Hilbert space. General identifiability results for mixtures in Hilbert spaces are established and applied to the Bayes Hilbert space setting. Building on these results, we propose a penalised maximum likelihood approach for the unmixing of Bayes Hilbert mixtures aimed at recovering the statistically space-efficient representation, together with a computationally efficient coordinate-wise maximisation algorithm for its implementation. The methodology is illustrated through a hyperspectral data application, where observations can be naturally embedded in the Bayes Hilbert space and analyzed in terms of distributional shape rather than amplitude. A complementary simulation study demonstrates the interpretability and practical performance of the proposed approach. |
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67/2026 - 28/08/2026
Ciardulli, S.; Fontana, N.; Vantini, S.; Ieva F.
Generalized propensity score weighting for functional causal inference framework | Abstract | | Estimating causal effects in observational studies requires adjustment for confounding, a task that becomes challenging when the exposure is a function observed over a continuous domain rather than a scalar variable. We develop a functional propensity score weighting framework that achieves covariate balance by removing dependence between time-varying treatments and observed confounders, thereby enabling estimation of marginal causal effects in settings with functional treatments, covariates, and outcomes.
We propose a dual formulation of the weight estimation problem that yields a smooth unconstrained optimization and improves computational scalability. The proposed framework extends naturally to settings with time-varying covariates and to longitudinal outcomes via a function-on-function marginal structural model, allowing estimation of causal effect surfaces. The proposed method improves covariate balance, estimation accuracy, and computational efficiency compared to the existing approach and retains these properties when extended to functional covariates and outcomes. We apply the method to data from the UK Biobank to estimate the causal effect of body mass index trajectories on the risk of Type 2 Diabetes and on subsequent glycated hemoglobin trajectories, a functional measure of metabolic status. |
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66/2026 - 28/07/2026
Antonietti, P.F.;Bonizzoni, F.;Corti, M.; De March, N.;Di Noto, S.;Regazzoni, F.
A stability-preserving polytopal discontinuous Galerkin method for the Fisher-Kolmogorov model with applications to neurodegenerative diseases | Abstract | | The Fisher-Kolmogorov model is one of the most widely used models in the study of neurodegenerative diseases, owing to its simple structure as a nonlinear reaction-diffusion equation. In particular, it is commonly employed to describe proteinopathies such as Alzheimer's and Parkinson's diseases. Under suitable assumptions, non-negativity of the solution is guaranteed at the continuous level, which is physically relevant since the solution represents a relative concentration. However, this property is not generally preserved at the discrete level, potentially leading to unphysical and unstable numerical approximations.
In this work, we analyze a modified version of the Fisher-Kolmogorov model that stabilizes the dynamics around the unstable equilibrium c=0. For the spatial discretization, we adopt a discontinuous Galerkin method on polygonal and polyhedral meshes, coupled with the Crank--Nicolson scheme for time integration. We derive stability and a-priori error estimates for the semi-discrete problem.
The theoretical findings are supported by numerical experiments, including convergence studies in both two and three dimensions. Finally, we validate the model through simulations of alpha-synuclein diffusion in a two-dimensional agglomerated brain section, demonstrating the high-order accuracy and robustness of the proposed method. |
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65/2026 - 17/07/2026
De Sanctis, M.F.; Arnone, E.; Ieva, F.; Sangalli, L.M.
Modeling group heterogeneity in spatio-temporal data via physics-informed regression | Abstract | | We propose a physics-informed semiparametric framework for modeling spatiotemporal data with group structure. The approach extends classical mixed effects regression by incorporating a nonparametric space–time component, regularized through a partial differential equation to encode the underlying physical dynamics, while random effects capture group-specific variability. Estimation is carried out via a two-step procedure based on a functional extension of the Iteratively Reweighted Least Squares algorithm. We establish asymptotic properties of both fixed and random effect estimators and we assess the performance of the method through simulation studies against state-of-the-art alternatives. The proposed framework is applied to hourly nitrogen dioxide data over Lombardy (Italy), where random effects account for measurement heterogeneity across monitoring stations with different sensor technologies, demonstrating its effectiveness in capturing both physical dynamics and group heterogeneity. |
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64/2026 - 15/07/2026
Radisic, I.; Tirotta, R.; Zingaro, A.; Pagani, S.; Dede', L.
Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics | Abstract | | Accurate, spatially resolved flow field measurements are essential for the reliable assessment of hemodynamic quantities in cardiovascular research and clinical practice. Experimental techniques, such as 4D flow MRI, PIV, or Doppler ultrasound, often yield data that are sparse, noisy, or under-resolved, particularly near vessel walls and in regions of complex flow. This limits the fidelity of distributed or derived hemodynamic indicators such as the wall shear stress and the clinical utility of such measurements. To address these challenges, we propose a physics-informed neural network (PINN) framework that integrates the incompressible Navier-Stokes equations with velocity measurements coming from experimental flow field data. By embedding physical laws into data, PINN enhances the reconstruction of velocity fields, enables the estimation of unmeasured quantities such as pressure and wall shear stress, and improves the spatial resolution of hemodynamic indicators. We show the effectiveness of our approach using both in silico and experimental data. First, we apply our method to the FDA nozzle benchmark, leveraging both control particle image velocimetry (PIV) measurements and computational fluid dynamics (CFD) simulations. Next, we apply our method to the more complex case of blood flow in an aneurysm model, exploiting in vitro 4D flow MRI data. In both cases, the synergy between data-driven learning and physics-based regularization yields results that align more closely with ground truth observations than standard CFD or pure data-driven approaches. Our findings highlight the potential of PINNs to improve the fidelity of under-resolved flow field measurements and yield spatially resolved hemodynamic indicators. |
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63/2026 - 01/07/2026
Ciaramella, G.; Gong, W.; Kwok, F., Tan, Z.
Uniform Convergence of the Schwarz Alternating Method for Optimal Control Problems | Abstract | | In this paper, we analyze the Schwarz alternating method for unconstrained elliptic optimal control problems, which is equivalent to the corresponding method for the associated saddle-point systems. A distinctive feature in this setting is that the local error propagation operators are not necessarily nonexpansive in the energy norm, which stands in marked contrast to the standard elliptic boundary-value case. We develop a rigorous uniform convergence theory in the continuous setting and then extend the analysis to finite difference discretizations. In both formulations, we prove that the Schwarz iteration converges whenever its counterpart for the underlying elliptic equation is convergent. Furthermore, we show that the contraction factor for the auxiliary elliptic equation in the maximum norm provides a uniform upper bound, which is independent of the regularization parameter $alpha$, for the contraction factor of the optimal control iteration in the same norm. The theoretical framework is also extended to cover one-level alternating Schwarz and parallel Schwarz variants. Numerical experiments are presented to validate the theoretical results. |
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62/2026 - 01/07/2026
Ciaramella, G.; Gong, W.; Kwok, F.; Tan, Z.
On the Uniform Convergence Analysis of the Schwarz Alternating Method for Optimal Control Problems | Abstract | | In this paper, we investigate the uniform convergence of the Schwarz alternating method for unconstrained elliptic optimal control problems in one dimension. We derive the convergence factor of the method and find that the convergence factor of the method can be uniformly bounded by a factor ($<1$) associated with that for the state equation. We also observe that the local error propagation operators of the method under a standard choice of energy norms in the robust analysis of optimal control problems are nonexpansive. These observations indicate that the existing convergence analysis frameworks of domain decomposition methods for PDEs based on the standard choice of energy norms are not straightforwardly applicable to that for optimal control problems. |
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