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 1249 prodotti
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37/2020 - 24/05/2020
Fumagalli, A.; Scotti, A.
A mathematical model for thermal single-phase flow and reactive transport in fractured porous media | Abstract | | In this paper we present a mathematical model and a numerical workflow for the simulation of a thermal single-phase flow with reactive transport in porous media, in the presence of fractures. The latter are thin regions which might behave as high or low permeability channels depending on their physical parameters, and are thus of paramount importance in underground flow problems. Chemical reactions may alter the local properties of the porous media as well as the fracture walls, changing the flow path and possibly occluding some portions of the fractures or zones in the porous media. To solve numerically the coupled problem we propose a temporal splitting scheme so that the equations describing each physical process are solved sequentially. Numerical tests shows the accuracy of the proposed model and the ability to capture complex phenomena, where one or multiple fractures are present. |
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36/2020 - 24/05/2020
Pellagatti, M.; Masci, C.; Ieva, F.; Paganoni A.M.
Generalized Mixed-Effects Random Forest: a flexible approach to predict university student dropout | Abstract | | We propose a new statistical method, called Generalized Mixed-Effects Random Forest (GMERF), that extends the use of random forest to the analysis of hierarchical data, for any type of response variable in the exponential family, considering both continuous and discrete covariates and without assuming a closed form in the association between the response and the fixed-effects covariates. At the same time GMERF takes into consideration the nested structure of hierarchical data, modelling
the latent grouping structure that exists in the higher level of the hierarchy and allowing statistical inference on this structure. In the case study, we apply GMERF to Higher Education data to analyse the university students dropout; in particular, we are interested in predicting students dropout probability given students-level information and considering the degree program they are enrolled in as the grouping factor. |
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35/2020 - 24/05/2020
Morbiducci, U.; Mazzi, V.; Domanin, M.; De Nisco, G.; Vergara, C.; Steinman, D.A.; Gallo, D.
Wall shear stress topological skeleton independently predicts long-term restenosis after carotid bifurcation endarterectomy | Abstract | | Wall shear stress (WSS) topological skeleton, composed by fixed points and the manifolds linking them, reflects the presence of blood flow features associated to adverse vascular response. However, the influence of WSS topological skeleton on vascular pathophysiology is still underexplored. This study aimed to identify direct associations between the WSS topological skeleton and markers of vascular disease from real-world clinical longitudinal data of long-term restenosis after carotid endarterectomy (CEA).
Personalized computational hemodynamic simulations were performed on a cohort of 13 carotid models pre-CEA and at 1 month after CEA. At 60 months after CEA, intima-media thickness (IMT) was measured to detect long-term restenosis. The analysis of the WSS topological skeleton was carried out by applying a Eulerian method based on the WSS vector field divergence. To provide objective thresholds for WSS topological skeleton quantitative analysis, a computational hemodynamic dataset of 46 ostensibly healthy carotid bifurcation models was considered.
CEA interventions did not completely restore physiological WSS topological skeleton features. Significant associations emerged between IMT at 60 months follow-up and the exposure to (1) high temporal variation of WSS contraction/expansion (R2=0.51, p<0.05), and (2) high fixed point residence times, weighted by WSS contraction/expansion strength (R2=0.53, p<0.05). These WSS topological skeleton features were statistically independent from the exposure to low WSS, a previously reported predictor of long-term restenosis, therefore representing different hemodynamic stimuli and potentially impacting differently the vascular response. This study confirms the direct association between WSS topological skeleton and vascular response, contributing to elucidate the mechanistic link between flow disturbances and clinical observations of vascular lesions.
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34/2020 - 24/05/2020
Antonietti, P.F.; Botti, M.; Mazzieri, I.; Nati Poltri, S.
A high-order discontinuous Galerkin method for the poro-elasto-acoustic problem on polygonal and polyhedral grids | Abstract | |
The aim of this work is to introduce a discretization of the physical phenomenon of propagation of acoustic waves through poroelastic materials, by exerting a finite element discontinuous Galerkin method on polygonal meshes. Wave propagation is modeled by the acoustics equations in the acoustic domain and the low-frequency Biot’s equations in the poroelastic one. The coupling is introduced by considering (physically consistent) interface conditions, imposed on the interface between the domains, modelling both open and sealed pores. Existence and uniqueness is proven for the strong formulation based on employing the Hille-Yosida theorem. For the space discretization we introduce a discontinuous Galerkin method, which is then coupled with suitable time integration schemes, such as the leapfrog and the Newmark methods. A stability analysis both for the continuous problem and the semi-discrete one is presented and error estimates for the energy norm are derived. A wide set of numerical results obtained on test cases with manufactured solutions are presented in order to validate the error analysis. Examples of physical interest are also presented to test the capability of the proposed methods in practical cases.
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33/2020 - 18/05/2020
Centofanti, F.; Fontana, M.; Lepore, A.; Vantini, S.
Smooth LASSO Estimator for the Function-on-Function Linear Regression Model | Abstract | | A new estimator, named as S-LASSO, is proposed for the coefficient function of a functional linear regression model where values of the response function, at a given domain point, depends on the full trajectory of the covariate function. The
S-LASSO estimator is shown to be able to increase the interpretability of the model, by better locating regions where the coefficient function is zero, and to smoothly estimate non-zero values of the coeffcient function. The sparsity of the estimator is ensured by a functional LASSO penalty whereas the smoothness is provided by two roughness penalties. The resulting estimator is proved to be estimation and pointwise sign consistent. Via an extensive Monte Carlo simulation study, the estimation and predictive performance of the S-LASSO estimator are shown to be better than (or at worst comparable with) competing estimators already presented in the literature before. Practical advantages of the S-LASSO estimator are illustrated through the analysis of the well known Canadian weather and Swedish mortality data. |
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32/2020 - 18/05/2020
Menafoglio, A.; Sgobba, S.; Lanzano, G.; Pacor, F.
Simulation of seismic ground motion fields via object-oriented spatial statistics: a case study in Northern Italy | Abstract | | This work offers a novel methodological framework to address the problem of generating data-driven earthquake shaking fields at different vibration periods, which are key to support decision making and civil protection planning. We propose to analyse the entire profiles of spectral accelerations and project their information content to unsampled locations in the system, based on the theory of Object Oriented Spatial Statistics (O2S2). The proposed methodology combines a non-ergodic ground motion model (GMM) with a fully functional model for the residual term, the latter consisting of (i) the spatially-varying systematic effects due to source, site and path, and (ii) the remaining aleatory error. The proposed methodology allows to generate multiple shaking scenarios conditioned on the data, jointly and consistently for all the vibration periods, overcoming the intrinsic limitations of existing multivariate approaches to the problem. The approach is tested on a vast dataset of ground motion records collected in the study-area of the Po Plain (Northern Italy), for which a region-specific fully non-ergodic GMM was previously calibrated. Our validation tests demonstrate the potentiality of the approach, which is capable to effectively simulate spectral acceleration profiles, while keeping the ability to capture the main physical features of ground motion patterns in the region.
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31/2020 - 18/05/2020
Bernardi, M.S.; Africa, P.C.; de Falco, C.; Formaggia, L.; Menafoglio, A.; Vantini, S.
On the Use of Interfeometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards | Abstract | | Recent advances in satellite technologies, statistical and mathematical models and computational resources have paved the way for an operational use of satellite data in monitoring and forecasting natural hazards. We present a review of the use of satellite data for Earth observation in the context of geohazards preventive monitoring and disaster evaluation and assessment. We describe the techniques exploited to extract ground displacement information from satellite radar sensors images and the applicability of such data to the study of natural hazards such as landslides, earthquakes, volcanic activity, and ground subsidence. In this context, statistical techniques, ranging from time series analysis to spatial statistics, as well as continuum or discrete physics-based models, adopting deterministic or stochastic approaches, are irreplaceable tools for modeling and simulating natural hazards scenarios from a mathematical perspective. In addition to this, the huge amount of data nowadays collected and the complexity of the models and methods needed for an effective analysis set new computational challenges. The synergy among statistical methods, mathematical models, and optimized software, enriched with the assimilation of satellite data, is essential for building predictive and timely monitoring models for risk analysis. |
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30/2020 - 09/05/2020
Massi, M.C., Gasperoni, F., Ieva, F., Paganoni, A.M., Zunino, P., Manzoni, A., Franco, N.R., et al.
A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multinational cohort | Abstract | | REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. |
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