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 1191 products
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06/2024 - 01/24/2024
Antonietti, P.F., Bonetti, S., Botti, M., Corti, M., Fumagalli, I., Mazzieri, I.
lymph: discontinuous poLYtopal methods for Multi-PHysics differential problems | Abstract | | We present the library lymph for the finite element numerical discretization of coupled multi-physics problems. lymph is a Matlab library for the discretization of partial differential equations based on high-order discontinuous Galerkin methods on polytopal grids (PolyDG) for spatial discretization coupled with suitable finite-difference time marching schemes. The objective of the paper is to introduce the library by describing it in terms of installation, input/output data, and code structure, highlighting -- when necessary -- key implementation aspects related to the method. A user guide, proceeding step-by-step in the implementation and solution of a Poisson problem, is also provided. In the last part of the paper, we show the results obtained for several differential problems, namely the Poisson problem, the heat equation, and the elastodynamics system. Through these examples, we show the convergence properties and highlight some of the main features of the proposed method, i.e. geometric flexibility, high-order accuracy, and robustness with respect to heterogeneous physical parameters. |
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04/2024 - 01/22/2024
Torzoni, M.; Tezzele, M.; Mariani, S.; Manzoni, A.; Willcox, K.E.
A digital twin framework for civil engineering structures | Abstract | | The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availability. This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures. The asset-twin coupled dynamical system is encoded employing a probabilistic graphical model, which allows all relevant sources of uncertainty to be taken into account. In particular, the time-repeating observations-to-decisions flow is modeled using a dynamic Bayesian network. Real-time structural health diagnostics are provided by assimilating sensed data with deep learning models. The digital twin state is continually updated in a sequential Bayesian inference fashion. This is then exploited to inform the optimal planning of maintenance and management actions within a dynamic decision-making framework. A preliminary offline phase involves the population of training datasets through a reduced-order numerical model and the computation of a health-dependent control policy. The strategy is assessed on two synthetic case studies, involving a cantilever beam and a railway bridge, demonstrating the dynamic decision-making capabilities of health-aware digital twins. |
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03/2024 - 01/16/2024
Ciaramella, G.; Gander, M.J.; Vanzan, T.
A gentle introduction to interpolation on the Grassmann manifold | Abstract | | These notes originated from the authors’ effort while studying interpolation techniques on the Grassmann manifold. This has been a hot topic recently since it is an important tool in parametric reduced order modelling. Fortunately, there is an extensive literature available with seminal contributions both from the engineering and mathematical communities. More generally, the development of numerical methods involving manifolds is a very active research area.
Given all literature about interpolation on Grassmann manifold, the reader may immediately ask the following question: is there any need for an additional introductory manuscript? It is the authors’ belief that this is actually the case. The aim of these notes is to precisely fill a gap in the literature, by providing a reference which gently introduces numerical analysts to the very interesting research topic of interpolation on the Grassmann manifold. Indeed, on the one hand, the engineering literature often does not provide the necessary mathematical details needed by a numerical analyst to understand the subject and to solidly build new computational algorithms. On the other hand, manuscripts from the mathematical community, despite being seminal references, tend to be overwhelming in terms of details, and mathematically concepts that are often not familiar to numerical analysts approaching the topic for the first time. These notes are meant to be a first very gentle introduction to these numerical methods, before approaching the more organic references. Further, the notes are self-contained concerning the derivation of geodesics, the algorithms to compute the exponential and logarithmic maps, and interpolation algorithms on the Grassmann manifold. These mathematical results are all well-known, but the original proofs are scattered across several manu scripts, often using different notations and level of detail, so that their study may not be immediate. |
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02/2024 - 01/13/2024
Parolini, N.; Poiatti, A.; Vene', J.; Verani, M.
Structure-preserving neural networks in data-driven rheological models | Abstract | | In this paper we address the importance and the impact of employing structure preserving neural networks as surrogate of the analytical physics-based models typically employed to describe the rheology of non-Newtonian fluids in Stokes flows. In particular, we propose and test on real-world scenarios a novel strategy to build data-driven rheological models based on the use of Input-Output Convex Neural Networks (ICNNs), a special class of feedforward neural network scalar valued functions that are convex with respect to their inputs. Moreover, we show, through a detailed campaign of numerical experiments, that the use of ICNNs is of paramount importance to guarantee the well-posedness of the associated non-Newtonian Stokes differential problem. Finally, building upon a novel perturbation result for non-Newtonian Stokes problems, we study the impact of our data-driven ICNN based rheological model on the accuracy of the finite element approximation. |
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01/2024 - 01/10/2024
Criseo, M.; Fumagalli, I.; Quarteroni, A.; Marianeschi, S. M.; Vergara, C.
Computational haemodynamics for pulmonary valve replacement by means of a reduced Fluid-Structure Interaction model | Abstract | | Pulmonary Valve Replacement (PVR) consists of substituting a patient’s original valve with a prosthetic one, primarily addressing pulmonary valve insufficiency, which is crucially relevant in Tetralogy of Fallot repairment. While extensive clinical and computational literature on aortic and mitral valve replacements is available, PVR's post-procedural haemodynamics in the pulmonary artery and the impact of prosthetic valve dynamics remain significantly understudied. Addressing this gap, we introduce a reduced Fluid-Structure Interaction (rFSI) model, applied for the first time to the pulmonary valve. This model couples a three-dimensional computational representation of pulmonary artery haemodynamics with a one-degree-of-freedom model to account for valve structural mechanics. Through this approach, we analyse patient-specific haemodynamics pre and post PVR. Patient-specific geometries, reconstructed from CT scans, are virtually equipped with a template valve geometry. Boundary conditions for the model are established using a lumped-parameter model, fine-tuned based on clinical patient data. Our model accurately reproduces patient-specific haemodynamic changes across different scenarios: pre-PVR, six months post-PVR, and a follow up condition after a decade. It effectively demonstrates the impact of valve implantation on sustaining the diastolic pressure gradient across the valve. Preliminary outcomes indicate the reliability of our valve model concerning the robustness of its application across various patients, despite being calibrated initially with data from a single patient. This promising approach provides insights into post-PVR haemodynamics and prosthetic valve effects, shedding light on potential implications for patient-specific outcomes. |
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106/2023 - 12/22/2023
Fontana, N.; Savaré, L.; Ieva, F.
Integrating state-sequence analysis to uncover dynamic drug-utilization patterns to profile heart failure patients | Abstract | | Globally, the incidence of heart failure is increasing, and its principal treatment involves drug therapy. However, widespread non-adherence to therapies is prevalent among heart failure patients and often results in worsening health conditions and an increase in hospital admissions. This study aims to develop an innovative approach, the State-Sequence analysis, to profile heart failure patients based on different drug-utilization patterns. These patterns aim to capture both the multidimensional and dynamic effects of therapies. Subsequently, the study explores how combining clustering algorithms with this technique influences overall patient survival. Findings highlight the importance of continued drug therapy after the first hospitalization in improving heart failure prognosis, irrespective of its severity. The proposed approach can assist healthcare specialists in evaluating the pathways provided to patients, allowing for a change in analysis from a transversal and syntactical approach to a holistic one that leverages statistical tools that are slightly more complex than traditional methods. Moreover, because of the many options available for defining states, temporal granularity, and spacing metrics, SSA is a flexible method applicable to various epidemiological scenarios. |
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109/2023 - 12/22/2023
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 - 12/22/2023
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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