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 1357 products
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67/2025 - 11/10/2025
Antonietti, P.F.; Corti, M.; Gómez S.; Perugia, I.
Structure-preserving local discontinuous Galerkin discretization of conformational conversion systems | Abstract | | We investigate a two-state conformational conversion system and introduce a novel structure-preserving numerical scheme that couples a local discontinuous Galerkin space discretization with the backward Euler time-integration method. The model is first reformulated in terms of auxiliary variables involving suitable nonlinear transformations, which allow us to enforce positivity and boundedness at the numerical level. Then, we prove a discrete entropy-stability inequality, which we use to show the existence of discrete solutions, as well as to establish the convergence of the scheme by means of some discrete compactness arguments. As a by-product of the theoretical analysis, we also prove the existence of global weak solutions satisfying the system's physical bounds. Numerical results validate the theoretical results and assess the capabilities of the proposed method in practice. |
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66/2025 - 11/09/2025
Speroni, G.; Mondini, N.; Ferro, N.; Perotto, S.
A topology optimization framework for scaffold design in soilless cultivation | Abstract | | In this work, we customize the microSIMPATY algorithm, combining Solid Isotropic Material with Penalization (SIMP) topology optimization with anisotropic mesh adaptation, within a multi-physics framework to design cellular material scaffolds suitable for soilless cultivation systems.
The design of these novel substrates is driven by optimization criteria that balance mechanical and fluid-dynamic performance, with the aim of effectively supporting plant growth.
The numerical validation results and the in silico root growth simulations carried out in virtual vase-like containers confirm promising potential toward replacing conventional organic and inorganic substrates with an optimized, sustainable, and readily accessible alternative. |
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65/2025 - 10/27/2025
Pottier, A.; Gelardi, F.; Larcher, A.; Capitanio, U.; Rainone, P.; Moresco, R.M.; Tenace, N.; Colecchia, M.; Grassi, S.; Ponzoni, M.; Chiti, A.; Cavinato, L.
MOSAIK: A computational framework for theranostic digital twin in renal cell carcinoma | Abstract | | In nuclear oncology, radiopharmaceuticals (RP) emerged as theranostic tools able to bind specifically to cancer biomarkers and inflict subsequently systematic and irreparable damage to the DNA of the targeted cells. That is why radioisotopes-
based therapies entered the clinical practice to diagnose and treat tumours simultaneously and may potentially overcome
therapeutic resistance encountered in various cancers. Despite these advancements, tumoural heterogeneity and poor anti-
cancer drug penetration in solid tumours turns out to be overlooked pieces of the personalized oncology puzzle, leading to
treatment failure. In this study, we propose MOSAIK, an oncological digital twin framework to simulate the intra-tumour uptake of radiopharmaceutical agent, specifically [89Zr]Zr-girentuximab in clear cell Renal Cell Carcinoma (ccRCC). Our comprehensive approach integrates patient-based insights in space and time for reflecting the multi-faceted nature of RP uptake. We develop models to segment blood vessels and identify neoplastic regions, enabling the characterization of the
biological domain. To discuss the intra-tumour heterogeneity contribution to the drug diffusion process, we spatially correlate immunochemistry images-derived parameters with the baseline drug accumulation captured through micro PET
imaging. Additionally, the model is informed with temporal features leveraged from the compartmental model of the RP
agent. The presented Deep Learning (DL) framework incorporates interpretable spatial and temporal inputs stemming from
histopathology images. This work aims to provide a computational model with predictive capabilities in drug retention in
tissues to move beyond the one-size-fits-all paradigm in nuclear medicine. |
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64/2025 - 10/21/2025
Celora, S.; Tonini, A.; Regazzoni, F.; Dede', L. Parati, G.; Quarteroni, A.
Cardiocirculatory Computational Models for the Study of Hypertension | Abstract | | In this work, we develop patient-specific cardiocirculatory models with the aim of building Digital Twins for hypertension. In particular, in our pathophysiology-based framework, we consider both 0D cardiocirculatory models and a 3D-0D electromechanical model. The 0D model, which consists of an RLC circuit, is studied in two variants, with and without capillaries. The 3D--0D model consists of a three-dimensional electromechanical model of the left ventricle, coupled with a 0D model for the external blood circulation: this representation enables the assessment of additional quantities related to ventricular deformation and stress, and offers a more detailed representation compared to a fully 0D model. Sensitivity analysis is performed on the 0D model, with both a mono- and a multi-parametric approach, in order to identify the parameters that most influence the model outputs and guide the calibration process. We studied three different scenarios, corresponding to systemic, pulmonary and renovascular hypertension, each in three nuances of severity. To maintain a fair comparison among the models, a parameter calibration strategy is developed; the outputs of the 0D model with capillaries are utilized to enhance the 3D-0D model. The results demonstrate that the 3D-0D model yields an accurate representation of cardiocirculatory dynamics in the presence of hypertension; this model represents a powerful step toward digital twins for real-time hypertension control, providing refined and clinically meaningful insights beyond those achievable with 0D models alone. |
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63/2025 - 10/14/2025
Panzeri, S.; Clemente, A.; Arnone, E.; Mateu, J.; Sangalli, L.M.
Spatio-Temporal Intensity Estimation for Inhomogeneous Poisson Point Processes on Linear Networks: A Roughness Penalty Method | Abstract | | Nowadays, a vast amount of georeferenced data pertains to human and natural activities occurring in complex network-constrained regions, such as road or river networks. In this article, our research focuses on spatio-temporal point patterns evolving over time on linear networks, which we model as inhomogeneous Poisson point processes. Within this framework, we propose an innovative nonparametric method for intensity estimation that leverages penalized maximum likelihood with roughness penalties based on differential operators applied across space and time. We provide an efficient implementation of the proposed method, relying on advanced computational and numerical techniques that involve finite element discretizations on linear networks. We validate the method through simulation studies conducted across various scenarios, evaluating its performance compared to state-of-the-art competitors. Finally, we illustrate the method through an application to road accident data recorded in the municipality of Bergamo, Italy, during the years 2017–2019. |
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62/2025 - 10/10/2025
Rigamonti, V.; Torri, V.; Morris, S. K.; Ieva, F.; Giaquinto, C.; Donà, D.; Di Chiara, C.; Cantarutti, A.; CARICE study group
Real-World Effectiveness of Influenza Vaccination in Preventing Influenza and Influenza-Like Illness in Children | Abstract | | Background and objectives: Quadrivalent live attenuated influenza vaccines (LAIV-4) offer an alternative to inactivated influenza vaccines (IIV) for children aged 2–17 years, but data on their comparative effectiveness are limited. This study assessed vaccination rates and real-world effectiveness of LAIV-4 and IIV in preventing influenza and influenza-like illness (ILI) in Italian children during the 2022–2023 and 2023–2024 seasons.
Methods: We conducted a population-based cohort study of children aged 2–14 years from September 2022 to April 2024, using data from Pedianet, a pediatric primary care database of anonymized records from family pediatricians. Children vaccinated with LAIV-4 or IIV were compared to unvaccinated children. The primary outcome was any first influenza or ILI episode. Monthly vaccination incidence rates per 1000 person-months were calculated for each vaccine type. Hazard ratios (HRs) and their 95 % confidence intervals (CIs) for vaccine effectiveness (VE) were estimated using adjusted mixed-effects Cox models.
Results: A total of 65,545 (472,173 person-months) and 72,377 (527,348 person-months) children were included for the 2022–2023 and 2023–2024 seasons, respectively. Vaccination rates were 12.71 and 12.85 per 1000 person-months, respectively. Compared to unvaccinated children, LAIV-4 had an overall effectiveness of 43% (95% CI, 32%–53%), while IIV effectiveness was 54% (95% CI, 46%–61%). In 2022–2023, LAIV-4 (38% [95% CI, 12%–56%]) and IIV (49% [95% CI, 37%–58%]) had comparable effectiveness. In 2023–2024, LAIV-4 (40 % [95% CI, 25%–52%]) was slightly less effective than IIV (58% [95% CI, 44%–68%])(p = 0.048).
Conclusions: An overall moderate, comparable effectiveness of LAIV-4 and IIV in preventing influenza/ILI among Italian children was observed.
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61/2025 - 10/07/2025
Coclite, A; Montanelli Eccher, R.; Possenti, L.; Vitullo, P.; Zunino, P.
Mathematical modeling and sensitivity analysis of hypoxia-activated drugs | Abstract | | Hypoxia-activated prodrugs offer a promising strategy for targeting oxygen-deficient regions in solid tumors, which are often resistant to conventional therapies. However, modeling their behavior is challenging because of the complex interplay between oxygen availability, drug activation, and cell survival. In this work, we develop a multiscale and mixed-dimensional model that couples spatially resolved drug and oxygen transport with pharmacokinetics and pharmacodynamics to simulate the cellular response. The model integrates blood flow, oxygen diffusion, and consumption, drug delivery, and metabolism. To reduce computational cost, we mitigate the global nonlinearity by coupling the multiscale and mixed-dimensional models one-way with a reduced 0D model for drug metabolization. The global sensitivity analysis is then used to identify key parameters influencing drug activation and therapeutic outcome. This approach enables efficient simulation and supports the design of optimized therapies targeting hypoxia. |
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60/2025 - 10/02/2025
Antonietti, P. F.; Caldana, M.; Gentile, L.; Verani M.
Deep Learning Accelerated Algebraic Multigrid Methods for Polytopal Discretizations of Second-Order Differential Problems | Abstract | | Algebraic Multigrid (AMG) methods are state-of-the-art algebraic solvers for partial differential equations. Still, their efficiency depends heavily on the choice of suitable parameters and/or ingredients. Paradigmatic examples include the so-called strong threshold parameter, which controls the algebraic coarse-grid hierarchy, as well as the smoother, i.e., the relaxation methods used on the fine grid to damp out high-frequency errors. In AMG, since the coarse grids are constructed algebraically (without geometric intuition), the smoother's performance is even more critical.
For the linear systems stemming from polytopal discretizations, such as Polytopal Discontinuous Galerkin (PolyDG) and Virtual Element Methods (VEM), AMG sensitivity to such choices is even more critical due to the significant variability of the underlying meshes, which results in algebraic systems with different sparsity patterns.
We propose a novel deep learning approach that automatically tunes the strong threshold parameter, as well as the smoother choice in AMG solvers, for linear systems of equations arising from polytopal discretizations, thereby maximizing AMG performance. We interpret the sparse matrix resulting from polytopal discretization as a grayscale image, and by applying pooling, our neural network extracts compact features that preserve the necessary information at a low computational cost.
We test various differential problems in both two- and three-dimensional settings, with heterogeneous coefficients and polygonal/polyhedral meshes, and demonstrate that the proposed approach generalizes well. In practice, we demonstrate that we can reduce AMG solver time by up to 27% with minimal changes to existing PolyDG and VEM codes. |
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