Publications

A complete list of my publications can be found in my Google scholar profile and on Scopus.

Preprints

1. L. Fabris, M. Tezzele, C. Busiello, M. Sicchiero, and G. Rozza, “Data-driven parameterization refinement for the structural optimization of cruise ship hulls”, arXiv:2411.09525, 2024.

2. L. Gkimisis, N. Aretz, M. Tezzele, T. Richter, P. Benner, and K. E. Willcox, “Non-intrusive reduced-order modeling for dynamical systems with spatially localized features”, Accepted on CMAME, 2025.

3. M. Tezzele, S. Carr, U. Topcu, and K. E. Willcox, “Adaptive planning for risk-aware predictive digital twins”, arXiv:2407.20490, 2024.

International Journals

1. A. Pecile, N. Demo, M. Tezzele, G. Rozza, and D. Breda, “Data-driven discovery of delay differential equations with discrete delays”, Journal of Computational and Applied Mathematics, vol. 461, p. 116439, 2025.  doi: 10.1016/j.cam.2024.116439.

2. F. Romor, M. Tezzele, and G. Rozza, “A Local Approach to Parameter Space Reduction for Regression and Classification Tasks”, Journal of Scientific Computing, vol. 99, no. 3, p. 83, 2024.  doi: 10.1007/s10915-024-02542-0.

3. M. Torzoni, M. Tezzele, S. Mariani, A. Manzoni, and K. E. Willcox, “A digital twin framework for civil engineering structures”, Computer Methods in Applied Mechanics and Engineering, vol. 418, p. 116584, Jan. 2024.  doi: 10.1016/j.cma.2023.116584.

4. N. Demo, M. Tezzele, and G. Rozza, “A DeepONet multi-fidelity approach for residual learning in reduced order modeling”, Advanced Modeling and Simulation in Engineering Sciences, vol. 10, no. 1, p. 12, 2023.  doi: 10.1186/s40323-023-00249-9.

5. F. Romor, M. Tezzele, M. Mrosek, C. Othmer, and G. Rozza, “Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering”, International Journal for Numerical Methods in Engineering, vol. 124, no. 23, pp. 5293–5311, Dec. 2023.  doi: 10.1002/nme.7349

6. M. Tezzele, L. Fabris, M. Sidari, M. Sicchiero, and G. Rozza, “A multi-fidelity approach coupling parameter space reduction and non-intrusive POD with application to structural optimization of passenger ship hulls”, International Journal for Numerical Methods in Engineering, vol. 124, no. 5, pp. 1193–1210, Mar. 2023.  doi: 10.1002/nme.7159.

7. F. Romor, M. Tezzele, A. Lario, and G. Rozza, “Kernel-based active subspaces with application to computational fluid dynamics parametric problems using discontinuous Galerkin method”, International Journal for Numerical Methods in Engineering, vol. 123, no. 23, pp. 6000–6027, Dec. 2022. doi: 10.1002/nme.7099.

8. N. Demo, M. Tezzele, A. Mola, and G. Rozza, “Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing”, Journal of Marine Science and Engineering, vol. 9, no. 2, p. 185, 2021.  doi: 10.3390/jmse9020185.

9. N. Demo, M. Tezzele, and G. Rozza, “A Supervised Learning Approach Involving Active Subspaces for an Efficient Genetic Algorithm in High-Dimensional Optimization Problems”, SIAM Journal on Scientific Computing, vol. 43, no. 3, B831–B853, 2021.  doi: 10.1137/20M1345219.

10. M. Gadalla, M. Cianferra, M. Tezzele, G. Stabile, A. Mola, and G. Rozza, “On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis”, Computers & Fluids, vol. 216, p. 104 819, 2021, issn: 0045-7930.  doi: 10.1016/j.compfluid.2020.104819.

11. M. Tezzele, N. Demo, G. Stabile, A. Mola, and G. Rozza, “Enhancing CFD predictions in shape design problems by model and parameter space reduction”, Advanced Modeling and Simulation in Engineering Sciences, vol. 7, no. 40, 2020.  doi: 10.1186/s40323-020-00177-y.

12. N. Demo, M. Tezzele, and G. Rozza, “A non-intrusive approach for reconstruction of POD modal coefficients through active subspaces”, Comptes Rendus Mécanique de l’Académie des Sciences, vol. 347, no. 11, pp. 873–881, Nov. 2019.  doi: 10.1016/j.crme.2019.11.012.

13. M. Tezzele, F. Salmoiraghi, A. Mola, and G. Rozza, “Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems”, Advanced Modeling and Simulation in Engineering Sciences, vol. 5, no. 1, p. 25, Sep. 2018, issn: 2213-7467.  doi: 10.1186/s40323-018-0118-3.

Conference Proceedings

1. S. Henao-Garcia, M. Kapteyn, K. E. Willcox, et al., “Digital-Twin-Enabled Multi-Spacecraft On-Orbit Operations,” in AIAA SCITECH 2025 Forum, American Institute of Aeronautics and Astronautics, Inc., 2025.  doi: 10.2514/6.2025-1432.

2. F. Romor, M. Tezzele, and G. Rozza, “Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces,” in Proceedings in Applied Mathematics & Mechanics, Wiley Online Library, vol. 20, 2021.  doi: 10.1002/pamm.202000349.

3. G. Rozza, M. H. Malik, N. Demo, et al., “Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018,” in ECCOMAS ECFD 7 - Proceedings of 6th European Conference on Computational Mechanics (ECCM 6) and 7th European Conference on Computational Fluid Dynamics (ECFD 7), R. Owen, R. de Borst, J. Reese, and P. Chris, Eds., Glasgow, UK, 2020, pp. 59–76.

4. N. Demo, M. Tezzele, A. Mola, and G. Rozza, “A complete data-driven framework for the efficient solution of parametric shape design and optimisation in naval engineering problems,” in Proceedings of MARINE 2019: VIII International Conference on Computational Methods in Marine Engineering, R. Bensow and J. Ringsberg, Eds., 2019, pp. 111–121.

5. A. Mola, M. Tezzele, M. Gadalla, et al., “Efficient reduction in shape parameter space dimension for ship propeller blade design,” in Proceedings of MARINE 2019: VIII International Conference on Computational Methods in Marine Engineering, R. Bensow and J. Ringsberg, Eds., 2019, pp. 201–212.

6. M. Tezzele, N. Demo, and G. Rozza, “Shape optimization through proper orthogonal decomposition with interpolation and dynamic mode decomposition enhanced by active subspaces,” in Proceedings of MARINE 2019: VIII International Conference on Computational Methods in Marine Engineering, R. Bensow and J. Ringsberg, Eds., 2019, pp. 122–133.

7. D. Cangelosi, A. Bonvicini, M. Nardo, et al., “SRtP 2.0 — The Evolution of the Safe Return to Port Concept,” in Technology and Science for the Ships of the Future: Proceedings of NAV 2018: 19th International Conference on Ship & Maritime Research, IOS Press, 2018, pp. 665–672.  doi: 10.3233/978-1-61499-870-9-665.

8. N. Demo, M. Tezzele, G. Gustin, G. Lavini, and G. Rozza, “Shape optimization by means of proper orthogonal decomposition and dynamic mode decomposition,” in Technology and Science for the Ships of the Future: Proceedings of NAV 2018: 19th International Conference on Ship & Maritime Research, IOS Press, 2018, pp. 212–219.  doi: 10.3233/978-1-61499-870-9-212.

9. N. Demo, M. Tezzele, A. Mola, and G. Rozza, “An efficient shape parametrisation by free-form deformation enhanced by active subspace for hull hydrodynamic ship design problems in open source environment,” in Proceedings of ISOPE 2018: The 28th International Ocean and Polar Engineering Conference, vol. 3, 2018, pp. 565–572.

10. M. Tezzele, N. Demo, M. Gadalla, A. Mola, and G. Rozza, “Model order reduction by means of active subspaces and dynamic mode decomposition for parametric hull shape design hydrodynamics,” in Technology and Science for the Ships of the Future: Proceedings of NAV 2018: 19th International Conference on Ship & Maritime Research, IOS Press, 2018, pp. 569–576.  doi: 10.3233/978-1-61499-870-9-569.

11. F. Salmoiraghi, F. Ballarin, G. Corsi, A. Mola, M. Tezzele, and G. Rozza, “Advances in geometrical parametrization and reduced order models and methods for computational fluid dynamics problems in applied sciences and engineering: Overview and perspectives,” in ECCOMAS Congress 2016 - Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering, vol. 1, Crete, Greece, 2016, pp. 1013–1031.  doi: 10.7712/100016.1867.8680.

Proceedings in Invited Books as Chapters and Chapters in Books

1. E. Donadini, M. Strazzullo, M. Tezzele, and G. Rozza, “A Data-Driven Partitioned Approach for the Resolution of Time-Dependent Optimal Control Problems with Dynamic Mode Decomposition,” in Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2020+1, J. M. Melenk, I. Perugia, J. Schöberl, and C. Schwab, Eds., Cham: Springer International Publishing, 2023, pp. 225–238. doi: 10.1007/978-3-031-20432-6_13.

2. N. Demo, M. Tezzele, G. Stabile, and G. Rozza, “Scientific Software Development and Packages for Reduced Order Models in Computational Fluid Dynamics,” in Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, ser. CS&E Series, G. Rozza, G. Stabile, and F. Ballarin, Eds., SIAM Press, 2022, ch. 19.  doi: 10.1137/1.9781611977257.ch19.

3. M. W. Hess, M. Tezzele, and G. Rozza, “Overview and Motivation,” in Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, ser. CS&E Series, G. Rozza, G. Stabile, and F. Ballarin, Eds., SIAM Press, 2022, ch. 1.  doi: 10.1137/1.9781611977257.ch1.

4. L. Meneghetti, N. Shah, M. Girfoglio, et al., “A Deep Learning Approach to Improving Reduced Order Models,” in Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, ser. CS&E Series, G. Rozza, G. Stabile, and F. Ballarin, Eds., SIAM Press, 2022, ch. 20.  doi: 10.1137/1.9781611977257.ch20.

5. A. Mola, N. Demo, M. Tezzele, and G. Rozza, “Geometrical Parameterization and Morphing Techniques with Applications,” in Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, ser. CS&E Series, G. Rozza, G. Stabile, and F. Ballarin, Eds., SIAM Press, 2022, ch. 17. doi: 10.1137/1.9781611977257.ch17.

6. M. Tezzele, N. Demo, A. Mola, and G. Rozza, “An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics,” in Novel Mathematics Inspired by Industrial Challenges, ser. Mathematics in Industry 38, M. Günther and W. Schilders, Eds., Springer International Publishing, 2022.  doi: 10.1007/978-3-030-96173-2_7.

7. M. Tezzele, N. Demo, G. Stabile, and G. Rozza, “Nonintrusive Data-Driven Reduced Order Models in Computational Fluid Dynamics,” in Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, ser. CS&E Series, G. Rozza, G. Stabile, and F. Ballarin, Eds., SIAM Press, 2022, ch. 9, isbn: 978-1-611977-24-0.  doi: 10.1137/1.9781611977257.ch9.

8. M. Tezzele, F. Romor, and G. Rozza, “Reduction in Parameter Space,” in Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics, ser. CS&E Series, G. Rozza, G. Stabile, and F. Ballarin, Eds., SIAM Press, 2022, ch. 16.  doi: 10.1137/1.9781611977257.ch16.

9. F. Garotta, N. Demo, M. Tezzele, M. Carraturo, A. Reali, and G. Rozza, “Reduced Order Isogeometric Analysis Approach for PDEs in Parametrized Domains,” in Quantification of Uncertainty: Improving Efficiency and Technology: QUIET selected contributions, ser. Lecture Notes in Computational Science and Engineering, M. D’Elia, M. Gunzburger, and G. Rozza, Eds., vol. 137, Cham: Springer International Publishing, 2020, pp. 153–170, isbn: 978-3-030-48721-8.  doi: 10.1007/978-3-030-48721-8_7.

10. G. Rozza, M. Hess, G. Stabile, M. Tezzele, and F. Ballarin, “Basic Ideas and Tools for Projection-Based Model Reduction of Parametric Partial Differential Equations,” in Model Order Reduction, P. Benner, S. Grivet-Talocia, A. Quarteroni, G. Rozza, W. H. A. Schilders, and L. M. Silveira, Eds., vol. 2, Berlin, Boston: De Gruyter, 2020, ch. 1, pp. 1–47, isbn: 9783110671490.  doi: 10.1515/9783110671490-001.

11. M. Tezzele, F. Ballarin, and G. Rozza, “Combined parameter and model reduction of cardiovascular problems by means of active subspaces and POD-Galerkin methods,” in Mathematical and Numerical Modeling of the Cardiovascular System and Applications, ser. SEMA-SIMAI Series, D. Boffi, L. F. Pavarino, G. Rozza, S. Scacchi, and C. Vergara, Eds., vol. 16, Springer International Publishing, 2018, pp. 185–207.  doi: 10.1007/978-3-319-96649-6_8.

Software Papers

1. S. M. Ichinaga, F. Andreuzzi, N. Demo, et al., “PyDMD: A Python package for robust dynamic mode decomposition”, Journal of Machine Learning Research, vol. 25, no. 417, pp. 1–9, 2024. arXiv:2402.07463

2. F. Romor, M. Tezzele, and G. Rozza, “ATHENA: Advanced Techniques for High dimensional parameter spaces to Enhance Numerical Analysis,” Software Impacts, vol. 10, p. 100 133, 2021.  doi: 10.1016/j.simpa.2021.100133.

3. M. Tezzele, N. Demo, A. Mola, and G. Rozza, “PyGeM: Python Geometrical Morphing,” Software Impacts, vol. 7, p. 100 047, 2021, issn: 2665-9638.  doi: 10.1016/j.simpa.2020.100047.

4. M. Gadalla, M. Tezzele, A. Mola, and G. Rozza, “BladeX: Python Blade Morphing,” The Journal of Open Source Software, vol. 4, no. 34, p. 1203, 2019.  doi: 10.21105/joss.01203.

5. N. Demo, M. Tezzele, and G. Rozza, “EZyRB: Easy Reduced Basis method,” The Journal of Open Source Software, vol. 3, no. 24, p. 661, 2018.  doi: 10.21105/joss.00661.

6. N. Demo, M. Tezzele, and G. Rozza, “PyDMD: Python Dynamic Mode Decomposition,” The Journal of Open Source Software, vol. 3, no. 22, p. 530, 2018.  doi: 10.21105/joss.00530.