Giuseppe Aloe

55 posts

New MOX Report on “A hybrid reduced-order and high-fidelity discontinuous Galerkin Spectral Element framework for large-scale PMUT array simulations”

A new MOX Report entitled “A hybrid reduced-order and high-fidelity discontinuous Galerkin Spectral Element framework for large-scale PMUT array simulations” by Antonietti, P. F.; Abdalla, O. M. O.; Garroni, M. G.; Mazzieri, I.; Parolini, N. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/27-2026.pdf Abstract: Piezoelectric Micromachined […]

New MOX Report on “Elimination-compensation pruning for fully-connected neural networks”

A new MOX Report entitled “Elimination-compensation pruning for fully-connected neural networks” by Ballini, E.; Muscarnera, L.; Fumagalli, A.; Scotti, A.; Regazzoni, F. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/23-2026.pdf Abstract: The unmatched ability of deep neural networks to capture complex patterns in large and noisy […]

New MOX Report on “Learning geometry-dependent lead-field operators for forward ECG modeling”

A new MOX Report entitled “Learning geometry-dependent lead-field operators for forward ECG modeling” by Dokuchaev, A.; Bonizzoni, F.; Pagani, S.; Regazzoni, F.; Pezzuto, S. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/26-2026.pdf Abstract: Modern forward electrocardiogram (ECG) computational models rely on an accurate representation of the […]

New MOX Report on “Neural Markov chain Monte Carlo: Bayesian inversion via normalizing flows and variational autoencoders”

A new MOX Report entitled “Neural Markov chain Monte Carlo: Bayesian inversion via normalizing flows and variational autoencoders” by Bottacini, G.; Torzoni, M.; Manzoni, A. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/21-2026.pdf Abstract: This paper introduces a Bayesian framework that combines Markov chain Monte Carlo […]

Model Reduction and Surrogate Modeling 2026

Organizers: Andrea Manzoni (Chair), Nicolò Botteghi, Luca Dede’, Nicola Farenga, Nicola Rares Franco, Andrea Manzoni, Simona Perotto, Piermario Vitullo, Paolo Zunino (Organizing Committee) When: 2026-11-2 – 2026-11-6 Where: Politecnico di Milano Description: This 5-day conference will bring together the international community of computational scientists, engineers, mathematicians, and domain experts from […]

New MOX Report on “DISARM++: Beyond scanner-free harmonization”

A new MOX Report entitled “DISARM++: Beyond scanner-free harmonization” by Caldera, L.; Cavinato, L.; Cirone, A.; Cama, I.; Garbarino, S.; Lodi, R.; Tagliavini, F.; Nigri, A.; De Francesco, S.; Cappozzo, A.; Piana, M.; Ieva, F.; has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/20-2026.pdf Abstract: Harmonization of […]

New MOX Report on “Scanner-agnostic MRI harmonization via SSIM-guided disentaglement”

A new MOX Report entitled “Scanner-agnostic MRI harmonization via SSIM-guided disentaglement” by Caldera, L.; Cavinato, L.; Ieva, F. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/19-2026.pdf Abstract: The variability introduced by differences in MRI scanner models, acquisition protocols, and imaging sites hinders consistent analysis and generalizability […]

New MOX Report on “MAGIC-Flow: multiscale adaptive conditional flows for generation and interpretable classification”

A new MOX Report entitled “MAGIC-Flow: multiscale adaptive conditional flows for generation and interpretable classification” by Caldera, L.; Bottacini, G.; Cavinato, L. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/17-2026.pdf Abstract: Generative modeling has emerged as a powerful paradigm for representation learning, but its direct applicability […]

New MOX Report on “MAGIC-Flow: multiscale adaptive conditional flows for generation and interpretable classification”

A new MOX Report entitled “MAGIC-Flow: multiscale adaptive conditional flows for generation and interpretable classification” by Caldera, L.; Bottacini, G.; Cavinato, L. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/17-2026.pdf Abstract: Generative modeling has emerged as a powerful paradigm for representation learning, but its direct applicability […]