MOX Reports
The preprint collection of the Laboratory for Modeling and Scientific Computation MOX. It mainly contains works on numerical
analysis and mathematical modeling applied to engineering problems. MOX web site is mox.polimi.it
Found 1242 products
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30/2020 - 05/09/2020
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 - 05/09/2020
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 - 05/08/2020
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 - 05/08/2020
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 - 05/08/2020
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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25/2020 - 04/16/2020
Calvetti, D.; Cosmo, A.; Perotto, S.; Somersalo, E.
Bayesian mesh adaptation for estimating distributed parameters | Abstract | | The problem of estimating numerically a distributed parameter from indirect measurements arises in many applications, and in that context the choice of the discretization plays an important role. In fact, to guarantee a certain level of accuracy of the forward model that maps the unknown to the observations may require a fine discretization, adding to the complexity of the problem and to the computational cost. On the other hand, reducing the complexity of the problem by adopting a coarser discretization may increase the modeling error and can be very detrimental for ill-posed inverse problems. To balance accuracy and complexity, we propose an adaptive algorithm for adjusting the discretization level automatically and dynamically while estimating the unknown distributed parameter by an iterative scheme. In the Bayesian paradigm, all unknowns, including the metric that defines the discretization, are modeled as random variables. Our approach couples the discretization with a Bayesian hierarchical hyperparameter that is estimated simultaneously with the unknown parameter of primary interest. The viability of the proposed algorithm, the Bayesian Mesh Adaptation (BMA) is assessed on two test cases, a fan-beam X-ray tomography and an inverse source problem for a Darcy flow model. |
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24/2020 - 04/16/2020
Formaggia, L.; Scotti, A.; Fumagalli, A.
Numerical Methods for Flow in Fractured Porous Media | Abstract | | In this work we present the mathematical models for single-phase flow in fractured porous media. An overview of the most common approaches is considered, which includes continuous fracture models and discrete fracture models. For the latter, we discuss strategies that are developed in literature for its numerical solution mainly related to the geometrical relation between the fractures and porous media grids. |
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23/2020 - 04/16/2020
Spreafico, M.; Ieva, F.
Functional modelling of recurrent events on time-to-event processes | Abstract | | In clinical practice many situations can be modelled in the framework of recurrent events. It is often the case where the association between the occurrence of events and time-to-event outcomes is of interest. The purpose of our study is to enrich the information available for modelling survival with relevant dynamic features, properly taking into account their possibly time-varying nature, as well as to provide a new setting for quantifying the association between time-varying processes and time-to-event outcomes. We propose an innovative methodology to model information carried out by time-varying processes by means of functional data. The main novelty we introduce consists in modelling each time-varying variable as the compensator of marked point process the recurrent events are supposed to derive from. By means of Functional Principal Component Analysis (FPCA), a suitable dimensional reduction of these objects is carried out in order to plug them into a survival Cox regression model. We applied our methodology to data retrieved from the administrative databases of Lombardy Region (Italy), related to patients hospitalized for Heart Failure (HF) between 2000 and 2012. We focused on time-varying processes of HF hospitalizations and multiple drugs consumption and we studied how they influence patients’ long-term survival. The introduction of this novel way to account for time-varying variables allowed for modelling self-exciting behaviours, for which the occurrence of events in the past increases the probability of a new event, and to make personalized predictions, quantifying the effect of personal behaviours and therapeutic patterns on survival. |
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