Contact information
| Position | Research Scientist |
| Phone | +39 06 50299 335 |
| Email | |
| Website | https://aserani.github.io/ |
| Office | Rome HQ |
| Address | Via di Vallerano 139, 00128 Rome, Italy |
| Research profiles | Google Scholar | Scopus | ORCID | ResearchGate | Publons | CNR People | GitHub |
Short biography
| Andrea Serani is a Research Scientist at the Institute of Marine Engineering of the National Research Council of Italy (CNR-INM), Rome. His research focuses on computational engineering design, simulation-based design optimization, design-space learning and dimensionality reduction, physics-aware machine learning, surrogate and multi-fidelity modelling, uncertainty quantification, and multidisciplinary optimization, with applications to marine, naval, underwater, and aerospace vehicle design. He received his B.Sc. and M.Sc. degrees in Mechanical Engineering and his Ph.D. from Roma Tre University in 2009, 2012, and 2016, respectively. Since joining CNR-INM, his research has progressively expanded from simulation-based and multidisciplinary vehicle optimization toward the development of reduced and data-driven representations of complex engineering design spaces. He is the lead developer of Parametric Model Embedding (PME), a methodology for constructing compact and interpretable representations of parametric design spaces while retaining an explicit mapping between reduced coordinates and the original design variables. His research has subsequently extended this framework toward physics-informed and physics-driven dimensionality reduction, and more recently toward nonlinear representations and design-manifold learning. More broadly, his current research investigates how geometry, physics, data, and optimization can be integrated to enable efficient exploration and automated design of complex engineering systems. Serani has been actively involved in the NATO Science and Technology Organization – Applied Vehicle Technology (AVT) Panel since 2013. He currently serves as Co-Chair of the AVT-404 Research Task Group on Machine Learning and Artificial Intelligence for Vehicle Design and contributes to international activities on data-driven and computational methods for advanced vehicle design. In 2026, he received the NATO STO AVT Young Contributor Award for his scientific and technical contributions to simulation-based design optimization, multi-fidelity modelling, uncertainty quantification, reduced-order approaches, and machine-learning-enhanced vehicle design. He is currently a Visiting Scholar at the University of Michigan, where he collaborates on computational methods for marine vehicle design and develops research activities connected with the U.S. Office of Naval Research (ONR). His previous international research experience includes visiting research periods at the University of Iowa and collaborations with academic, governmental, and industrial partners in Europe and the United States. Alongside his research activity at CNR-INM, he is Adjunct Professor at the University of Bologna, where he teaches Computer-Aided Yacht Design within the M.Sc. programme in Nautical Engineering. His teaching and mentoring activities include the supervision and co-supervision of graduate students and early-career researchers working on computational design, optimization, machine learning, and marine engineering. He is author or co-author of more than 100 scientific publications and conference contributions and regularly serves as reviewer for international journals in computational mechanics, engineering design, optimization, and marine engineering. He has reviewed more than 140 manuscripts for international journals and has contributed to the scientific community through editorial activities, conference organization, and invited lectures. |
Research interests
| Simulation-based design optimization Design-space learning and dimensionality reduction Physics-aware machine learning Surrogate modelling Multi-fidelity methods Optimization under uncertainty Computational fluid dynamics Multidisciplinary engineering design |
Research topics/groups
| Multidisciplinary analysis and optimization (MAO) Computational fluid dynamics (CFD) |
Selected publications
- Serani A., Diez M. (2026). A Survey on Design-Space Dimensionality Reduction Methods for Shape Optimization. Archives of Computational Methods in Engineering, 33, 1671–1698.
- Serani A., Palma G., Wackers J., Quagliarella D., Gaggero S., Diez M. (2025). Extending parametric model embedding with physical information for design-space dimensionality reduction in shape optimization. Engineering with Computers, 41, 4643–4663.
- Serani A., Scholcz T.P., Vanzi V. (2024). A Scoping Review on Simulation-Based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps. Archives of Computational Methods in Engineering, 31, 4709–4737.
- Serani A., Diez M., Quagliarella D. (2024). Aerodynamic shape optimization in transonic conditions through parametric model embedding. Aerospace Science and Technology, 155, 109611.
- Serani, A., Diez, M. (2023). Parametric model embedding. Computer Methods in Applied Mechanics and Engineering, 404, 115776.
- Serani, A., Dragone, P., Stern, F., Diez, M. (2023). On the use of dynamic mode decomposition for time-series forecasting of ships operating in waves. Ocean Engineering, 267, 113235.
- Serani, A., Stern, F., Campana, E. F., & Diez, M. (2022). Hull-form stochastic optimization via computational-cost reduction methods. Engineering with Computers. 38(3), 2245-2269.
- Serani, A., Diez, M., van Walree, F., & Stern, F. (2021). URANS analysis of a free-running destroyer sailing in irregular stern-quartering waves at sea state 7. Ocean Engineering, 237, 109600.
- D’Agostino D., Serani A., & Diez M. (2020). Design-space assessment and dimensionality reduction: An off-line method for shape reparameterization in simulation-based optimization. Ocean Engineering, 197, 106852.
- Serani A., Pellegrini R., Wackers J., Jeanson C. E., Queutey P., Visonneau M., & Diez M. (2019). Adaptive multi-fidelity sampling for CFD-based optimisation via radial basis function metamodels. International Journal of Computational Fluid Dynamics, 33(6-7), 237-255.
- Serani A., Leotardi C., Iemma U., Campana E. F., Fasano G., & Diez M. (2016). Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems. Applied Soft Computing, 49, 313-334.
- Serani A., Fasano G., Liuzzi G., Lucidi S., Iemma U., Campana E. F., Stern F., & Diez M. (2016). Ship hydrodynamic optimization by local hybridization of deterministic derivative-free global algorithms. Applied Ocean Research, 59, 115-128.