The shape and mass optimization can make the submerged structures, for instance hull appendages of ships and components of marine renewable energy systems, particularly sensitive to external loads. This sensitivity influences several properties, including wear, fatigue and stealthiness. The related economic issues (definition of refined design margins and reduction of damage probabilities or acoustic discomfort) are of paramount importance for the entire shipbuilding industry, both civil and military. The crucial point for a reliable design in these domains lies in a relevant assessment of each parameter influence: intensity and direction of current, characteristics of materials and mechanical loads. In industry, the design of structures is evaluated by capitalizing on a very limited number of configurations and scenarios, mainly derived from the intuition and practice of designers. For optimized structures or new concepts, by definition without any feedback, the risk of under-sizing is recognized. An appropriate design requires knowledge of relevant parameters and the impact of their variabilities. This information may be obtained for example via the resolution of numerous numerical simulations, by varying the parameter values. In industrial applications, this cannot be achieved with high-fidelity models, that require prohibitive CPU time. This is an important technological barrier, which can potentially be raised by the development and use of parametric reduced order models (ROM), if they combine speed, accuracy and reliability. The aim of the project MODUL'O PI is to develop such models, adapted to the underlying difficulties (high Reynolds numbers flows; complex submerged structures), and to foster the transfer to industrial applications, specially for wear, fatigue and acoustic stealthiness. Two main problems shall be solved to reach this objective: (P1) the influence on the hydrodynamic load of uncertainties related to the flow configurations; (P2) the influence on the vibratory level of uncertainties related to the loads and materials parameters. To tackle these issues, this project is divided into three tasks. Obtaining a reduced parametric formulation of the hydrodynamic wall pressure, problem (P1), will be the subject of tasks 1 and 2. A meta-model of the wall pressure will be built in task 1, with an approximation obtained through non-intrusive sampling approaches: statistical learning techniques and use of multi-fidelity models. In task 2, the ROM will be obtained through Galerkin methods and the variations of the parameters will be tackled by interpolation on Grassmannian manifold and enrichment via low rank approximation algorithms. The problem (P2), more applied by nature and oriented submerged structures dynamics, will be the subject of task 3. The technical difficulty comes from the high dimensionality of the parameter space: the reduced basis of the related ROM will be built iteratively with a low number of calls to the full model, thanks to a dedicated greedy algorithm and an efficient a posteriori error estimator. In the long run, the use of reduced-order models may be considered in the design phase of a ship or marine renewable energy installation. From the industrial standpoint, taking into account the parametric variabilities will ensure increased control of design margins and lead to longer life time and reliability of marine structures. The technical and economic benefits concern the reduction of manufacturing cost and those related to operational maintenance, ensuring the competitiveness of shipbuilders and the profitability of marine renewable energy installations.