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 1275 products
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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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59/2025 - 10/01/2025
Wolf, F.; Botteghi, N.; Fasel, U.; Manzoni, A.
Interpretable and efficient data-driven discovery and control of distributed systems | Abstract | | Effectively controlling systems governed by Partial Differential Equations (PDEs) is crucial in several fields of Applied Sciences and Engineering. These systems usually yield significant challenges to conventional control schemes due to their nonlinear dynamics, partial observability, high-dimensionality once discretized, distributed nature, and the requirement for low-latency feedback control. Reinforcement Learning (RL), particularly Deep RL (DRL), has recently emerged as a promising control paradigm for such systems, demonstrating exceptional capabilities in managing high-dimensional, nonlinear dynamics. However, DRL faces challenges including sample inefficiency, robustness issues, and an overall lack of interpretability. To address these issues, we propose a data-efficient, interpretable, and scalable Dyna-style Model-based RL framework for PDE control, combining the Sparse Identification of Nonlinear Dynamics with Control (SINDy-C) algorithm and an Autoencoder (AE) framework for the sake of dimensionality reduction of PDE states and actions. This novel approach enables fast rollouts, reducing the need for extensive environment interactions, and provides an interpretable latent space representation of the PDE forward dynamics. To address these challenges, we propose a data-efficient, interpretable, and scalable Dyna-style Model-based Reinforcement Learning framework specifically tailored for PDE control. Our approach integrates Sparse Identification of Nonlinear Dynamics with Control within an Autoencoder-based dimensionality reduction scheme for PDE states and actions (AE+SINDy-C). This combination enables fast rollouts with significantly fewer environment interactions, while providing an interpretable latent space representation of the PDE dynamics, facilitating insight into the control process. We validate our method on two PDE problems describing fluid flows - namely, the 1D Burgers equation and 2D Navier-Stokes equations - comparing it against a model-free baseline. Our extensive analysis highlights improved sample efficiency, stability, and interpretability in controlling complex PDE systems. |
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58/2025 - 09/23/2025
Pivato, C.A.; Cozzi, O.; Fontana, N.; Ieva, F., et al.
Clinical outcomes of percutaneous coronary interventions after transcatheter aortic valve replacement | Abstract | | The number of patients undergoing percutaneous coronary interventions (PCI) after transcatheter aortic valve replacement (TAVR) is expected to increase, but their prognosis remains poorly understood.
Consecutive PCI patients with prior TAVR were compared to patients without prior TAVR between 2008 and 2023. The Kaplan–Meier method was used to estimate the 1-year incidence of major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death or myocardial infarction. An entropy balance approach was implemented to adjust for imbalances in patient and procedural characteristics. Adjusted hazard ratios (HRs) were estimated using weighted Cox regression models. Comparing 420 PCI patients with prior TAVR (mean age 80.8 years, 37.1% women) to 1197 without (mean age 70.4 years, 24.6% women), 1-year MACE was higher in the prior TAVR group (8.7 vs. 3.7%; unadjusted HR 2.35, 95% CI 1.49–3.69; P < 0.001). After adjustment for clinical and procedural characteristics, prior TAVR remained associated with an increased risk of MACE (adjusted HR 2.36, 95% CI 1.08–5.16; P = 0.032). This was primarily driven by higher cardiovascular death (adjusted HR 3.12, 95% CI 1.10–8.79, P = 0.032), while the association with myocardial infarction was attenuated post-adjustment and no longer statistically significant.
Patients undergoing PCI after TAVR experience a higher incidence of MACE compared to those undergoing PCI without prior TAVR, underscoring the importance of accurate patient selection before performing PCI in patients with chronic coronary syndrome and history of TAVR. |
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