|Matteo Diez is a Senior Research Scientist at CNR-INM, National Research Council-Institute of Marine Engineering, Rome, Italy, where he leads a research group in multidisciplinary analysis and optimization. His research focuses on simulation-based design optimization methodologies, aiming at the affordable use of high-fidelity prime-principle based solvers in ship hydrodynamics and fluid structure interaction for both deterministic and stochastic applications. He is author of more than 100 papers published in peer-reviewed international journals (including Computer Methods in Applied Mechanics and Engineering, Engineering with Computers, Ocean Engineering, Structural Multidisciplinary Optimization, Marine Structures, Applied Soft Computing, ASME Journal of Verification, Validation and Uncertainty Quantification) and international conference proceedings. He is Editorial Board Member at Nature Portfolio‘s Scientific Reports. He has been Adjunct Professor at the Università Iuav di Venezia, Università degli Studi Roma Tre, and the Istituto Nazionale di Architettura, IN/ARCH. He has been Visiting Research Scholar at the University of Iowa under ONR support. He has been co-chair and technical team member of several activities on deterministic and stochastic optimization for vehicle design within NATO Science and Technology Organization Applied Vehicle Technology Panel. He has been principal investigator of ONR-funded NICOP (Naval International Cooperative Opportunities in Science and Technology Program) grants. He received his M.Sc. degree in Mechanical Engineering from Università degli Studi Roma Tre in 2003 and his Ph.D. degree in Mechanical and Industrial Engineering from the same University in 2007, with a thesis on multidisciplinary optimization methods for conceptual aircraft design.|
|Simulation-based design optimization in ship hydrodynamics; Uncertainty quantification and reliability-based robust design optimization; Design space assessment and dimensionality reduction in hydrodynamic shape optimization; Global derivative-free bio-inspired optimization algorithms; Fluid-structure interaction and multidisciplinary design optimization; Dynamic metamodelling|
|Multidisciplinary Analysis and Optimization (MAO)|
- Serani, A. and Diez, M., 2023. Parametric model embedding. Computer Methods in Applied Mechanics and Engineering, 404, p.115776.
- Serani, A., Dragone, P., Stern, F. and Diez, M., 2023. On the use of dynamic mode decomposition for time-series forecasting of ships operating in waves. Ocean engineering, 267, p.113235.
- Wackers, J., Pellegrini, R., Serani, A., Visonneau, M. and Diez, M., 2022. Efficient initialization for multi-fidelity surrogate-based optimization. Journal of Ocean Engineering and Marine Energy, pp.1-17.
- D’Agostino, D., Serani, A., Stern, F. and Diez, M., 2022. Time-series forecasting for ships maneuvering in waves via recurrent-type neural networks. Journal of Ocean Engineering and Marine Energy, 8(4), pp.479-487.
- Diez, M., Serani, A., Campana, E.F. and Stern, F., 2022. Time-series forecasting of ships maneuvering in waves via dynamic mode decomposition. Journal of Ocean Engineering and Marine Energy, 8(4), pp.471-478.
- Pellegrini, R., Wackers, J., Broglia, R., Serani, A., Visonneau, M. and Diez, M., 2022. A multi-fidelity active learning method for global design optimization problems with noisy evaluations. Engineering with Computers, pp.1-24.
- Diez, M., Lee, E.J., Harrison, E.L., Powers, A.M.R., Snyder, L.A., Jiang, M.J., Bay, R.J., Lewis, R.R., Kubina, E.R., Mucha, P. and Stern, F., 2022. Experimental and computational fluid-structure interaction analysis and optimization of deep-V planing-hull grillage panels subject to slamming loads–Part I: Regular waves. Marine Structures, 85, p.103256.
- Serani, A., Stern, F., Campana, E.F. and Diez, M., 2022. Hull-form stochastic optimization via computational-cost reduction methods. Engineering with Computers, 38(3), pp.2245-2269.
- Khan, S., Kaklis, P., Serani, A. and Diez, M., 2022. Geometric moment-dependent global sensitivity analysis without simulation data: application to ship hull form optimisation. Computer-Aided Design, p.103339.
- Khan, S., Kaklis, P., Serani, A., Diez, M. and Kostas, K., 2022. Shape-supervised dimension reduction: Extracting geometry and physics associated features with geometric moments. Computer-Aided Design, p.103327.