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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62/2020 - 09/14/2020
Massi, M. C.; Ieva, F.
Representation Learning Methods for EEG Cross-Subject Channel Selection and Trial Classification | Abstract | | EEG is a non-invasive powerful system that finds applications in several domains and research areas. At the moment, most EEG systems require subjects to wear several electrodes on the scalp. However, a large number of channels might include noisy information, redundant signals, induce longer preparation times and increase the computational times of any automated system trying to classify EEG recordings. One way to reduce the signal-to-noise ratio and improve the classification accuracy is to combine channel selection with feature extraction. However, when dealing with EEG channel selection most of the efforts have been focused on identifying the most relevant channels in a subject-dependent fashion. In this paper we introduce a novel algorithm for subject independent channel selection of EEG recordings.
In particular, the algorithm (i) exploits channel-specific Representation Learning Methods to compress signals from various channels, (ii) builds a unique representation of each trial by concatenating the channels' representations into a unique low-dimensional vector and (iii) selects from these vectors the most relevant channels to perform classification. After training, the algorithm can be exploited by (iv) transferring the parametrized subgroup of selected channel-specific RLMs to new signals and (v) obtain novel trial vectors to be fed to any kind of classifier. We tested the algorithm on a case study attaining extremely promising results when considering the complexity of subject independent channel selection. |
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61/2020 - 09/10/2020
Pozzi, S.; Redaelli, A.; Vergara, C.; Votta, E.; Zunino, P.
Mathematical and numerical modeling of atherosclerotic plaque progression based on fluid-structure interaction | Abstract | | In this work we propose a mathematical and numerical model to describe the early stages of atherosclerotic plaque formation,
which is based on the interaction of processes with different spatial and temporal scales.
A fluid-structure interaction problem, used to describe the cardiovascular mechanics arising between blood and the artery wall, is coupled to a set of differential problems describing the evolution of solute concentrations.
In order to manage the multiscale-in-space nature of the involved processes, we propose a suitable numerical strategy based on the splitting and sequential solution of the coupled problem.
We present some preliminary numerical results and investigate the effects of geometry, model parameters and coupling strategy on plaque growth. |
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60/2020 - 08/19/2020
Lupo Pasini, M; Perotto, S.
Hierarchical model reduction driven by a Proper Orthogonal Decomposition for parametrized advection-diffusion-reaction problems | Abstract | | This work combines the Hierarchical Model (HiMod) reduction technique with a standard Proper Orthogonal Decomposition (POD) to solve parametrized partial differential equations modeling advection-diffusion-reaction phenomena in elongated domains (e.g., pipes). This combination leads to what we define a HiPOD model reduction, which merges the reliability of HiMod with the computational efficiency of POD. Two different HiPOD techniques are presented and assessed through an extensive numerical verification. |
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59/2020 - 08/07/2020
Massi, M.C.; Franco, N.R; Ieva, F.; Manzoni, A.; Paganoni, A.M.; Zunino, P.
High-Order Interaction Learning via Targeted Pattern Search | Abstract | | Logistic Regression (LR) is a widely used statistical method in empirical studies in many research fields. However, these real-life scenarios oftentimes share complexities that would hinder the application of the as-is model. First and foremost, the need to include high-order interactions to capture the variability of their data. Moreover, these studies are seldom developed in imbalanced settings, with datasets growing wider, sample size
from very large to extremely small and a strong need for model and results interpretability.
In this paper we present a novel algorithm, High-Order Interaction Learning via targeted Pattern search (HOILP), to select interaction terms of varying order to include in a LR for
an imbalanced binary classification task when input data is categorical. HOILP’s rationale is built on the duality between item sets and categorical interactions, and is composed of
(i) an interaction learning step based on a well-known frequent item set mining algorithm and (ii) a novel dissimilarity-based interaction selection step, that allows the user to control
for the number of interactions to include in the LR model. Besides HOILP we present here two variants (Scores HOILP and Clusters HOILP), that can suit even more specific needs.
Through a set of experiments we validate our algorithm and prove its wide applicability to real-life research scenarios, surpassing the performance of a benchmark state-of-the-art
algorithm. |
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58/2020 - 08/07/2020
Beraha, M.; Pegoraro, M.; Peli, R.; Guglielmi, A
Spatially dependent mixture models via the Logistic Multivariate CAR prior | Abstract | | We consider the problem of spatially dependent areal data, where for each
area independent observations are available, and propose to model
the density of each area through a finite mixture of Gaussian distributions.
The spatial dependence is introduced via a novel joint distribution for
a collection of vectors in the simplex, that we term logisticMCAR.
We show that salient features of the logisticMCAR distribution
can be described analytically, and that a suitable augmentation scheme based on the
P{'o}lya-Gamma identity allows to derive an efficient Markov Chain Monte Carlo
algorithm.
When compared to competitors, our model has proved to better estimate densities in different (disconnected) areal locations when they have different characteristics.
We discuss an application on a real dataset of Airbnb listings in the city
of Amsterdam, also showing how to easily incorporate for additional covariate
information in the model. |
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57/2020 - 08/07/2020
Regazzoni, F.; Quarteroni, A.
An oscillation-free fully partitioned scheme for the numerical modeling of cardiac active mechanics | Abstract | | In silico models of cardiac electromechanics couple together mathematical models describing different physics. One instance is represented by the model describing the generation of active force, coupled with the one of tissue mechanics. For the numerical solution of the coupled model, partitioned schemes, that foresee the sequential solution of the two subproblems, are often used. However, this approach may be unstable. For this reason, the coupled model is commonly solved as a unique system using Newton type algorithms, at the price, however, of high computational costs. In light of this motivation, in this paper we propose a new numerical scheme, that is numerically stable and accurate, yet within a fully partitioned (i.e. segregated) framework. Specifically, we introduce, with respect to standard segregated scheme, a numerically consistent stabilization term, capable of removing the nonphysical oscillations otherwise present in the numerical solution of the commonly used segregated scheme. Our new method is derived moving from a physics-based analysis on the microscale energetics of the force generation dynamics. By considering a model problem of active mechanics we prove that the proposed scheme is unconditionally absolutely stable (i.e. it is stable for any time step size), unlike the standard segregated scheme, and we also provide an interpretation of the scheme as a fractional step method. We show, by means of several numerical tests, that the proposed stabilization term successfully removes the nonphysical numerical oscillations characterizing the non stabilized segregated scheme solution. Our numerical tests are carried out for several force generation models available in the literature, namely the Niederer-Hunter-Smith model, the model by Land and coworkers, and the mean-field force generation model that we have recently proposed. Finally, we apply the proposed scheme in the context of a three-dimensional multiscale electromechanical simulation of the left ventricle. |
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56/2020 - 08/07/2020
Botti, L.; Botti, M.; Di Pietro, D. A.;
A Hybrid High-Order method for multiple-network poroelasticity | Abstract | | We develop Hybrid High-Order methods for multiple-network poroelasticity, modelling seepage through deformable fissured porous media. The proposed methods are designed to support general polygonal and polyhedral elements. This is a crucial feature in geological modelling, where the need for general elements arises, e.g., due to the presence of fracture and faults, to the onset of degenerate elements to account for compaction or erosion, or when nonconforming mesh adaptation is performed. We use as a starting point a mixed weak formulation where an additional total pressure variable is added, that ensures the fulfilment of a discrete inf-sup condition. A complete theoretical analysis is performed, and the results are demonstrated on a panel of numerical tests. |
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55/2020 - 08/07/2020
Botti, M.; Castanon Quiroz, D.; Di Pietro, D.A.; Harnist, A.
A Hybrid High-Order method for creeping flows of non-Newtonian fluids | Abstract | | In this paper, we design and analyze a Hybrid High-Order discretization method for the steady motion of non-Newtonian, incompressible fluids in the Stokes approximation of small velocities. The proposed method has several appealing features including the support of general meshes and high-order, unconditional inf-sup stability, and orders of convergence that match those obtained for Leray--Lions scalar problems.
A complete well-posedness and convergence analysis of the method is carried out under new, general assumptions on the strain rate-shear stress law, which encompass several common examples such as the power-law and Carreau--Yasuda models. Numerical examples complete the exposition. |
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