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 1349 prodotti
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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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29/2020 - 09/05/2020
Piersanti, R.; Africa, P.C.; Fedele, M.; Vergara, C.; Dede', L.; Corno, A.F.; Quarteroni, A.
Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations
| Abstract | | Since myocardial fibers drive the electric signal propagation throughout the myocardium, accurately modeling their arrangement is essential for simulating heart electrophysiology (EP). Rule-Based-Methods (RBMs) represent a commonly used strategy to include cardiac fibers in computational models. A particular class of such methods is known as Laplace-Dirichlet-Rule-Based-Methods (LDRBMs) since they rely on the solution of Laplace problems. In this work we provide a unified framework, based on LDRBMs, for generating full heart muscle fibers. We first present a unified description for existing ventricular LDRBMs, introducing some modeling improvements with respect to the existing literature. We then carry out a systematic comparison of LDRBMs based on meaningful biomarkers produced by numerical EP simulations. Next we propose, for the first time, a LDRBM to be used for generating atrial fibers. The new method, tested both on idealized and realistic atrial models, can be applied to any arbitrary geometries. Finally, we present numerical results obtained in a realistic whole heart where fibers are included for all the four chambers using the discussed LDRBMs. |
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28/2020 - 08/05/2020
Regazzoni, F.; Dedè, L.; Quarteroni, A.
Biophysically detailed mathematical models of multiscale cardiac active mechanics | Abstract | | We propose four novel mathematical models, describing the microscopic mechanisms of force generation in the cardiac muscle tissue, which are suitable for multiscale numerical simulations of cardiac electromechanics. Such models are based on a biophysically accurate representation of the regulatory and contractile proteins in the sarcomeres. Our models, unlike most of the sarcomere dynamics models that are available in the literature and that feature a comparable richness of detail, do not require the time-consuming Monte Carlo method for their numerical approximation. Conversely, the models that we propose only require the solution of a system of PDEs and/or ODEs (the most reduced of the four only involving 20 ODEs), thus entailing a significant computational efficiency. By focusing on the two models that feature the best trade-off between detail of description and identifiability of parameters, we propose a pipeline to calibrate such parameters starting from experimental measurements available in literature. Thanks to this pipeline, we calibrate these models for room-temperature rat and for body-temperature human cells. We show, by means of numerical simulations, that the proposed models correctly predict the main features of force generation, including the steady-state force-calcium and force-length relationships, the tension-dependent prolongation of twitches, the force-velocity relationship. Moreover, they correctly reproduce the Frank-Starling effect, when employed in multiscale 3D numerical simulation of cardiac electromechanics. |
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27/2020 - 08/05/2020
Spreafico, M.; Ieva, F.; Fiocco, M.
Modelling dynamic covariates effect on survival via Functional Data Analysis: application to the MRC BO06 trial in osteosarcoma | Abstract | | Time-varying covariates are of great interest in clinical research since they represent dynamic patterns which reflect disease progression. In cancer studies biomarkers values change as functions of time and chemotherapy treatment is modified by delaying a course or reducing the dose intensity, according to patient’s toxicity levels. Models for time-to-event to deal with the dynamic nature of time-varying covariates during follow-up are necessary, still not well developed. In this work, innovative methods to represent time-dependent covariates by means of Functional Data Analysis (FDA) and how to include them into survival models are discussed. This new approach was applied to osteosarcoma data from the MRC BO06/EORTC 80931 randomized clinical trial, new insights into the clinical research. Time-varying covariates related to alkaline phosphatase (ALP) and chemotherapy dose during treatment were considered. Processes dynamics over time were investigated and additional information that may be related to the survival were included into the time-to-event models. High ALP levels reflected poor overall survival. Although dose-intense profiles were not associated with a better survival, the strength of our method is the ability to detect differences between patients with different biomarker evolution and treatment response, even when randomised to the same regimen. This aspect is seldom addressed in the literature. |
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26/2020 - 08/05/2020
Zonca, S.; Antonietti, P.F.; Vergara, C.
A Polygonal Discontinuous Galerkin formulation for contact mechanics in fluid-structure interaction problems | Abstract | | In this work, we propose a formulation based on the Polygonal Discontinuous Galerkin (PolyDG) method for contact mechanics that arises in fluid-structure interaction problems. In particular, we introduce a consistent penalization approach to treat the frictionless contact between immersed structures that undergo large displacements. The key feature of the method is that the contact condition can be naturally embedded in the PolyDG formulation, allowing to easily support polygonal/polyhedral meshes. The proposed approach introduced a fixed background mesh for the fluid problem overlapped by the structure grid that is able to move independently of the fluid one. To assess the validity of the proposed approach, we report the results of several numerical experiments obtained in the case of contact between flexible structures immersed in a fluid. |
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