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mathematics for sustainable development

scientific coordinator: Luca Formaggia

 

The main goals for this topic are the development of mathematical models and numerical methods with applications to biomedicine (e.g., precision medicine, mathematical oncology, neurosciences) and for the sustainable use of the subsoil and mitigation of the effects of human activities (e.g., CO2 sequestration, geothermal reservoirs), as well as the implementation and analysis of scientific machine learning algorithms.

 

 

Publications

  • G. Ciaramella, F. Nobile, T. Vanzan, "A multigrid method for PDE-constrained optimization with uncertain inputs", preprint arXiv:2302.13680v3 [math.OC];
  • F. Gatti, S. Perotto, C. de Falco, L. Formaggia, "A parallel well-balanced numerical scheme for the simulation of fast landslides with efficient time stepping", preprint;
  • A. Agosti, E. Rocca, L. Scarpa, "Strict separation and numerical approximation for a non-local Cahn-Hilliard equation with single-well potential",  preprint arXiv:2306.15819 [math.AP];

2025

  • Colli, P. , Gilardi, G., Signori, A., Sprekels, J., 2025. Solvability and optimal control of a multi-species Cahn–Hilliard–Keller–Segel tumor growth model., ESAIM Control Optim. Calc. Var., https://doi.org/10.1051/cocv/2025070.
  • Antonietti, Paola F.; Corti, M. Martinelli, G.,Polytopal mesh agglomeration via geometrical deep learning for three-dimensional heterogeneous domains. Mathematics and Computers in Simulation, 241, Part B, 335-353 - https://doi.org/10.1016/j.matcom.2025.10.019.
  • Di Battista, I., De Sanctis, M. F., Arnone, E., Castiglione, C., Palummo, A., Sangalli, L. M., 2025, A semiparametric space-time quantile regression model, Journal of Nonparametric Statistics, 1–32, https://doi.org/10.1080/10485252.2025.2593910
  • Regazzoni, F. and Poggesi, C. and Ferrantini, C., 2025, Elucidating the cellular determinants of the end-systolic pressure-volume relationship of the heart via computational modelling, The Journal of Physiology, http://doi.org/10.1113/JP287282
  • Giardini, F., Olianti, C., Marchal, G. A., Campos, F., Steyer, J., Madl, J., Piersanti, R., Arecchi, G., Vanaja, I. P., Biasci, V., Nesi, G., Loew, L., Cerbai, E., Chelko, S., Regazzoni, F., Loewe, A., Bishop, M., Mongillo, M., Kohl, P., Zaglia, T., Johnston, C. M., Sacconi, L., 2025, Correlative imaging integrates electrophysiology with three-dimensional murine heart reconstruction to reveal electrical coupling between cell types, Nature Cardiovascular Research, 4: 1466-1486, https://doi.org/10.1038/s44161-025-00728-9
  • Tenderini, R. and Pegolotti, L. and Kong, F. and Pagani, S. and Regazzoni, F. and Marsden, A. L. and Deparis, S., 2025, Deformable registration and generative modelling of aortic anatomies by auto-decoders and neural ODEs, NPJ Biological Physics and Mechanics, 2: 26, https://doi.org/10.1038/s44341-025-00029-z
  • Antonietti, P.F., Caldana, M., Mazzieri, I., Re Fraschini, A., 2025, MAGNET: an open-source library for mesh agglomeration by graph neural networks. Engineering with Computers 41: 4825-4850. https://doi.org/10.1007/s00366-025-02223-y
  • Botti, M., Fumagalli, I., Mazzieri, I.,2025, Polytopal discontinuous Galerkin methods for low-frequency poroelasticity coupled to unsteady Stokes flow. Engineering with Computers 41: 4173-4189. https://doi.org/10.1007/s00366-025-02182-4
  • Antonietti, P.F, Botti, M., Cancrini, A., Mazzieri, I., 2025, A polytopal discontinuous Galerkin method for the pseudo-stress formulation of the unsteady Stokes problem, Computer Methods in Applied Mechanics and Engineering, 447: 118404, https://doi.org/10.1016/j.cma.2025.118404.
  • Botti, M., Fumagalli, I., Mazzieri, I., 2025, Polytopal discontinuous Galerkin methods for low-frequency poroelasticity coupled to unsteady Stokes flow, Engineering with Computers, 41:4173–4189, https://doi.org/10.1007/s00366-025-02182-4
  • Antonietti, P.F.; Dede', L.; Loli, G.; Montardini, M.; Sangalli, G.; Tesini, P., 2025,  Space–time Isogeometric Analysis of cardiac electrophysiology, Computer Methods in Applied Mechanics and Engineering, https://doi.org/10.1016/j.cma.2025.117957  
  • Bucelli, M.; Dede', L., 2025, Coupling Models of Resistive Valves to Muscle Mechanics in Cardiac Fluid–Structure Interaction Simulations, International Journal for Numerical Methods in Biomedical Engineering, 41(12):1-19, https://doi.org/10.1002/cnm.70119 
  • Bonetti, S., Botti, M., Vega, P., 2025, A robust fully-mixed finite element method with skew-symmetry penalization for low-frequency poroelasticity, Preprint, https://doi.org/10.48550/arXiv.2512.10192 
  • De Sanctis M.F., Arnone E., Ieva F., Sangalli L.M., 2025, Modeling group heterogeneity in spatio-temporal data via physics-informed semiparametric regression, Preprint, https://arxiv.org/abs/2511.13203 
  • Gilardi A., De Sanctis M.F., Milan G., Ieva F., Sangalli L.M., Secchi P., 2025 Three Spatial Methods for Assessing PM10 Concentration in the Lombardy Region, Statistics for Innovation I. SIS 2025, Springer, Cham, https://doi.org/10.1007/978-3-031-96736-8_56 
  • Rosafalco, L., Conti, P., Manzoni, A., Mariani, S., Frangi, A., 2025, Online learning in bifurcating dynamic systems via SINDy and Kalman filtering,. Nonlinear Dynamics, 113 (12): 14201-14221, https://doi.org/10.1007/s11071-025-11029-y,  
  • Farenga, N., Fresca, S., Brivio S., Manzoni, A., 2025, On latent dynamics learning in nonlinear reduced order modeling Neural Networks, 185: 107146, https://doi.org/10.1016/j.neunet.2025.107146,