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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109/2023 - 22/12/2023
Clementi, L.; Arnone, E.; Santambrogio, M.D.; Franceschetti, S.; Panzica, F.; Sangalli, L.M.
Anatomically compliant modes of variations: new tools for brain connectivity | Abstract | | Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects’ expressions along these common modes of variation differ between controls and pathological
subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients. |
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108/2023 - 22/12/2023
Arnone, E.; Negri, L.; Panzica, F.; Sangalli, L.M.
Analyzing data in complicated 3D domains: smoothing, semiparametric regression and functional principal component analysis | Abstract | | In this work we introduce a family of methods for the analysis of data observed at locations scattered in three-dimensional (3D) domains, with possibly complicated shapes. The proposed family of methods includes smoothing, regression and functional principal component analysis for functional signals defined over (possibly non-convex) 3D domains, appropriately complying with the non-trivial shape of the domain. This constitutes an important advance with respect to the literature, since the available methods to analyse data observed in 3D domains rely on Euclidean distances, that are inappropriate when the shape of the domain influences the phenomenon under study. The common building block of the proposed methods is a nonparametric regression model with differential regularization. We derive the asymptotic properties of the methods and show, through simulation studies, that they are superior to the available alternatives for the analysis of data in 3D domains, even when considering domains with simple shapes. We finally illustrate an application to a neurosciences study, with neuroimaging signals from functional magnetic resonance imaging, measuring neural activity in the grey matter, a non-convex volume with a highly complicated structure. |
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105/2023 - 16/12/2023
Cicci, L.; Fresca, S.; Guo, M.; Manzoni, A.; Zunino, P.
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression | Abstract | | Uncertainty quantification (UQ) tasks, such as sensitivity analysis and parameter estimation, entail a huge computational complexity when dealing with input-output maps involving the solution of nonlinear differential problems, because of the need to query expensive numerical solvers repeatedly. Projection-based reduced order models (ROMs), such as the Galerkin-reduced basis (RB) method, have been extensively developed in the last decades to overcome the computational complexity of high fidelity full order models (FOMs), providing remarkable speed-ups when addressing UQ tasks related with parameterized differential problems. Nonetheless, constructing a projection-based ROM that can be efficiently queried usually requires extensive modifications to the original code, a task which is non-trivial for nonlinear problems, or even not possible at all when proprietary software is used. Non-intrusive ROMs – which rely on the FOM as a black box – have been recently developed to overcome this issue. In this work, we consider ROMs exploiting proper orthogonal decomposition to construct a reduced basis from a set of FOM snapshots, and Gaussian process regression (GPR) to approximate the RB projection coefficients. Two different approaches, namely a global GPR and a tensor-decomposition-based GPR, are explored on a set of 3D time-dependent solid mechanics examples. Finally, the non-intrusive ROM is exploited to perform global sensitivity analysis (relying on both screening and variance-based methods) and parameter estimation (through Markov chain Monte Carlo methods), showing remarkable computational speed-ups and very good accuracy compared to high-fidelity FOMs. |
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101/2023 - 15/12/2023
Formaggia, L.; Zunino, P.
Hybrid dimensional models for blood flow and mass transport | Abstract | | Mathematical models accounting of several space scales have proved to be very effective tools in the description and simulation of the cardiovascular system. In this chapter, we review the family of models that are based on partial differential equations defined on domains with hybrid dimension. Referring to the vascular applications, the most prominent example consists of coupling three-dimensional (3D) with one-dimensional (1D) mathematical models for blood flow and mass transport. On the basis of their coupling conditions these models can be subdivided into two main categories: the ones based on sequential coupling and those arising from the embedded coupling. We organize this work in two main sections, reflecting this subdivision. |
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104/2023 - 15/12/2023
Possenti, L.; Gallo, A.; Vitullo, P.; Cicchetti, A.; Rancati, T.; Costantino, M.L.; Zunino, P.
A computational model of the tumor microenvironment applied to fractionated radiotherapy | Abstract | | Radiotherapy consists in delivering a precise radiation dose to a specific tumor target in order to eradicate tumor cells and to achieve local tumor control. The definition of the most suitable radiotherapy treatment schedule is not trivial due to the large tumor heterogeneity reported in clinical practice. The ultimate goal is to prescribe a specific treatment pattern for each patient, considering all the different radiobiological properties of the tumor / normal tissues to achieve the best final result possible. The model presented in this work goes in this direction, analyzing oxygen dependency and the role of the vascular network in the tumor microenvironment, since the efficacy of radiation therapy also depends on local oxygen availability. The main purpose of this work is to develop a mathematical model that describes the interaction between microvascular oxygen transfer and the efficacy of fractionated radiotherapy. |
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103/2023 - 15/12/2023
Dimola N.; Kuchta M.; Mardal K.A.; Zunino P.
Robust Preconditioning of Mixed-Dimensional PDEs on 3d-1d domains coupled with Lagrange Multipliers | Abstract | | In the context of micro-circulation, the coexistence of two distinct length scales - the vascular radius and the tissue/organ scale - with a substantial difference in magnitude, poses significant challenges. To handle slender inclusions and simplify
the geometry involved, a technique called topological dimensionality reduction is used, which suppresses the manifold dimensions associated with the smaller characteristic length. However, the algebraic structure of the resulting discretized system presents a challenge in constructing efficient solution algorithms. This chapter addresses this challenge by developing a robust preconditioner for the 3d-1d problem using the operator preconditioning technique. The robustness of the preconditioner is demonstrated with respect to the problem parameters, except for the vascular radius. The vascular radius, as demonstrated, plays a fundamental role in the mathematical well-posedness of the problem and the effectiveness of the preconditioner. |
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98/2023 - 14/12/2023
Lespagnol, F.; Grandmont, C.; Zunino, P.; Fernandez, M.A.
A mixed-dimensional formulation for the simulation of slender structures immersed in an incompressible flow | Abstract | | We consider the simulation of slender structures immersed in a three-dimensional (3D) flow. By exploiting the special geometric configuration of the slender structures, this particular problem can be modeled by mixed-dimensional coupled equations (3D for the fluid and 1D for the solid).
Several challenges must be faced when dealing with this type of problems. From a mathematical point of view, these include defining wellposed trace operators of codimension two. On the computational standpoint, the non-standard mathematical formulation makes it difficult to ensure the accuracy of the solutions obtained with the mixed-dimensional discrete formulation as compared to a fully resolved one.
We establish the continuous formulation using the Navier-Stokes equations for the fluid and a Timoshenko beam model for the structure. We complement these models with a mixed-dimensional version of the fluid-structure interface conditions, based on the projection of kinematic coupling conditions on a finite-dimensional Fourier space. Furthermore, we develop a discrete formulation within the framework of the finite element method, establish the energy stability of the scheme, provide extensive numerical evidence of the accuracy of the discrete formulation, notably with respect to a fully resolved (ALE based) model and a standard reduced modeling approach. |
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100/2023 - 14/12/2023
Vitullo, P.; Cicci, L.; Possenti, L.; Coclite, A.; Costantino, M.L.; Zunino, P.
Sensitivity analysis of a multi-physics model for the vascular microenvironment | Abstract | | The vascular microenvironment is the scale at which microvascular transport, interstitial tissue properties and cell metabolism interact. The vascular microenvironment has been widely studied by means of quantitative approaches, including multi-physics mathematical models as it is a central system for the pathophysiology of many diseases, such as cancer.
The microvascular architecture is a key factor for the fluid balance and mass transfer in the vascular microenvironment, together with the physical parameters characterizing the vascular wall and the interstitial tissue. The scientific literature of this field has witnessed a long debate about which factor of this multifaceted system is the most relevant.
The purpose of this work is to combine the interpretative power of an advanced multi-physics model of the vascular microenvironment with state of the art, robust sensitivity analysis methods, in order to determine what factors affect the most some quantity of interest, related in particular to cancer treatment. We are particularly interested in comparing the factors related to the microvascular architecture with the ones affecting the physics of microvascular transport.
Ultimately, this work will provide further insight of how the vascular microenvironment affects cancer therapies, such as chemotherapy, radiotherapy or immunotherapy. |
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