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 1148 products
-
78/2023 - 10/12/2023
Antonietti, P.F.; Bonizzoni, F.; Corti, M.; Dall'Olio, A.
Discontinuous Galerkin for the heterodimer model of prion dynamics in Parkinson's disease | Abstract | | Neurodegenerative diseases have a significant global impact affecting millions of individuals worldwide. Some of them, known as proteinopathies, are characterized by the accumulation and propagation of toxic proteins, known as prions. Alzheimer's and Parkinson's diseases are relevant of protheinopathies. Mathematical models of prion dynamics play a crucial role in understanding disease progression and could be of help to potential interventions. This article focuses on the heterodimer model: a system of two partial differential equations that describe the evolution of healthy and misfolded proteins. In particular, we propose a space discretization based on a Discontinuous Galerkin method on polygonal/polyhedral grids, which provides flexibility in handling meshes of complex brain geometries. Concerning the semi-discrete formulation we prove stability and a-priori error estimates. Next, we adopt a theta-method scheme for time discretization. Some convergence tests are performed to confirm the theoretical bounds and the ability of the method to approximate travelling wave solutions. The proposed scheme is also tested to simulate the spread of alpha-synuclein in a realistic test case of Parkinson's disease in a two-dimensional sagittal brain section geometry reconstructed from medical images. |
-
77/2023 - 10/11/2023
Fumagalli, I.; Corti, M.; Parolini, N.; Antonietti, P. F.
Polytopal discontinuous Galerkin discretization of brain multiphysics flow dynamics | Abstract | | A comprehensive mathematical model of the multiphysics flow of blood and Cerebrospinal Fluid (CSF) in the brain can be expressed as the coupling of a poromechanics system and Stokes' equations: the first describes fluids filtration through the cerebral tissue and the tissue's elastic response, while the latter models the flow of the CSF in the brain ventricles. This model describes the functioning of the brain's waste clearance mechanism, which has been recently discovered to play an essential role in the progress of neurodegenerative diseases. To model the interactions between different scales in the porous medium, we propose a physically consistent coupling between Multi-compartment Poroelasticity (MPE) equations and Stokes' equations. In this work, we introduce a numerical scheme for the discretization of such coupled MPE-Stokes system, employing a high-order discontinuous Galerkin method on polytopal grids to efficiently account for the geometric complexity of the domain. We analyze the stability and convergence of the space semidiscretized formulation, we prove a-priori error estimates, and we present a temporal discretization based on a combination of Newmark's beta-method for the elastic wave equation and the theta-method for the other equations of the model. Numerical simulations carried out on test cases with manufactured solutions validate the theoretical error estimates. We also present numerical results on a two-dimensional slice of a patient-specific brain geometry reconstructed from diagnostic images, to test in practice the advantages of the proposed approach. |
-
76/2023 - 10/06/2023
Ieva, F.; Galliani, G.; Secchi, P.
The impact of public transport on the diffusion of COVID-19 pandemie in Lombardy during 2020 | Abstract | | In 2020, the COVID-19 pandemic has impacted the world, affecting health, economy, education, and social behavior. Much concern was raised about the role of mobility in the diffusion of the disease, with particular attention to public transport. lndeed, understanding the relationship between mobility and the pandemic is key for developing effective public health interventions and policy decisions.
In this work, we aim to understand how mobility, and more specifically mobility by public transport, has affected the diffusion of the pandemic at the regional scale. We focus our attention on Lombardy, the most populated ltalian region severely hit by the pandemic in 2020. We explore static mobility data provided by Regione Lombardia, the regional service district, and dynamic mobility data provided by Trenord, a railway operator which serves Lombardy and neighboring areas. We develop an inventive pipeline for the dynamic estimation of Origin-Destination matrices obtained from tickets and passenger counts. This allows us to spot potential triggers in pandemic diffusion enhanced by the concept of proximity induced by mobility. We also develop a novel perspective for assessing the relationship between mobility and overal1 mortality based upon a functional approach combined with a spatial correlation analysis aimed at identifying the diversified effects on mortality in smal1 geographical areas as a result. |
-
75/2023 - 09/29/2023
Archetti, A.; Ieva, F.; Matteucci, M.
Scaling survival analysis in healthcare with federated survival forests: A comparative study on heart failure and breast cancer genomics | Abstract | | Survival analysis is a fundamental tool in medicine, modeling the time until an event of interest occurs in a population. However, in real-world applications, survival data are often incomplete, censored,
distributed, and confidential, especially in healthcare settings where privacy is critical. The scarcity of data can severely limit the scalability of survival models to distributed applications that rely on
large data pools. Federated learning is a promising technique that enables machine learning models to be trained on multiple datasets without compromising user privacy, making it particularly wellsuited
for addressing the challenges of survival data and large-scale survival applications. Despite significant developments in federated learning for classification and regression, many directions remain unexplored in the context of survival analysis. In this work, we propose an extension of the Federated Survival Forest algorithm, called FedSurF++. This federated ensemble method constructs random survival forests in heterogeneous federations. Specifically, we investigate several new tree
sampling methods from client forests and compare the results with state-of-the-art survival models based on neural networks. The key advantage of FedSurF++ is its ability to achieve comparable performance to existing methods while requiring only a single communication round to complete.
The extensive empirical investigation results in a significant improvement from the algorithmic and privacy preservation perspectives, making the original FedSurF algorithm more efficient, robust, and
private. We also present results on two real-world datasets – a heart failure dataset from the Lombardy HFData project and Fed-TCGA-BRCA from the Falmby suite – demonstrating the success of FedSurF++ in real-world healthcare studies. Our results underscore the potential of FedSurF++ to improve the scalability and effectiveness of survival analysis in distributed settings while preserving user privacy. |
-
74/2023 - 09/29/2023
Pidò, S.; Pinoli, P.; Crovari, P.; Ieva, F.; Garzotto, F.; Ceri, S.
Ask Your Data—Supporting Data Science Processes by Combining AutoML and Conversational Interfaces | Abstract | | Data Science is increasingly applied for solving real-life problems, in industry and in academic research, but mastering Data Science requires an interdisciplinary education that is still scarce on the market.
Thus, there is a growing need for user-friendly tools that allow domain experts to directly apply data analysis methods to their datasets, without involving a Data Science expert. In this scenario, we present DSBot, an assistant that can analyze the user data and produce answers by mastering several Data Science techniques.
DSBot understands the research question with the help of conversation interaction, produces a data science pipeline and automatically executes the pipeline in order to generate analysis. The strength of DSBot lies
in the design of a rich domain specific language for modeling data analysis pipelines, the use of a suitable neural network for machine translation of research questions, the availability of a vast dictionary of pipelines for matching the translation output, and the use of natural language technology provided by a conversational agent. We empirically evaluated the translation capabilities and the autoML performances of the system. In the translation task, it obtains a BLEU score of 0.8. In prediction tasks, DSBot outperforms TPOT, an autoML tool, in 19 datasets out of 30. |
-
71/2023 - 09/28/2023
Conni, G.; Piccardo, S.; Perotto, S.; Porta, G.M.; Icardi, M.
HiPhome: HIgh order Projection-based HOMogEnisation for advection diffusion reaction problems | Abstract | | We propose a new model reduction technique for multiscale scalar transport problems that exhibit dominant axial dynamics.
To this aim, we rely on the separation of variables to combine a Hierarchical Model (HiMod) reduction with a two-scale asymptotic expansion. We extend the two-scale asymptotic expansion to an arbitrary order and exploit the high-order correctors to define the HiMod modal basis which approximates the transverse dynamics of the flow, while we adopt a finite element discretisation to model the leading stream.
The resulting method, which is named HiPhome (HIgh-order Projection-based HOMogEnisation), is successfully assessed both in steady and unsteady advection-diffusion-reaction settings. The numerical results confirm the very good performance of HiPhome which improves the accuracy and the convergence rate of HiMod and extends the reliability of the standard homogenised solution to transient and pre-asymptotic regimes. |
-
70/2023 - 09/25/2023
Ragni, A.; Ippolito, D.; Masci, C.
Assessing the Impact of Hybrid Teaching on Students' Academic Performance via Multilevel Propensity Score-based techniques | Abstract | | This study employs multilevel propensity score techniques in an innovative analysis pipeline to assess the impact of hybrid teaching – a blend of face-to-face and online learning – on student performance within engineering programs at Politecnico di Milano. By analyzing students' credits earned and grade point average, the investigation compares outcomes of students engaged in hybrid teaching against those solely in face-to-face instruction that precedes the Covid-19 pandemic.
Tailored multilevel models for earned credits and grade point averages are fitted onto meticulously constructed dataframes, effectively minimizing potential biases stemming from variables such as gender, age at career initiation, previous academic track, admission test scores, and student origins across the two groups.
The methodology accounts for variations across distinct educational programs and investigates disparities among them.
Our findings suggest marginal overall disparities in student performance, indicating, on average, a subtle inclination toward a modest rise in earned credits and a slight decrease in grade point averages among those exposed to hybrid teaching. The use of multilevel models to analyze data within the same institution revealed that the impact of hybrid teaching on students' performances can vary significantly across different engineering programs, providing valuable insights into its effectiveness in diverse educational contexts. |
-
69/2023 - 09/20/2023
Ferro, N.; Micheletti, S.; Parolini, N.; Perotto, S.; Verani, M.; Antonietti, P. F.
Level set-fitted polytopal meshes with application to structural topology optimization | Abstract | | We propose a method to modify a polygonal mesh in order to fit the zero-isoline of a level set function by extending a standard body-fitted strategy to a tessellation with arbitrarily-shaped elements. The novel level set-fitted approach, in combination with a Discontinuous Galerkin finite element approximation, provides an ideal setting to model physical problems characterized by embedded or evolving complex geometries, since it allows skipping any mesh post-processing in terms of grid quality. The proposed methodology is firstly assessed on the linear elasticity equation, by verifying the approximation capability of the level set-fitted approach when dealing with configurations with heterogeneous material properties. Successively, we combine the level set-fitted methodology with a minimum compliance topology optimization technique, in order to deliver optimized layouts exhibiting crisp boundaries and reliable mechanical performances. An extensive numerical test campaign confirms the effectiveness of the proposed method. |
|
