8 papers
A Machine Learning Enabled MDO for Bio-Inspired Autonomous Underwater Gliders
Andrea Serani, Giorgio Palma, Jeroen Wackers +1
The preliminary design of AUGs is intrinsically challenging due to the strong coupling between the external hydrodynamic shape, the hydrostatic balance, the structural integrity, a…
A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design
Andrea Serani, Giorgio Palma, Matteo Diez
Dimensionality reduction is essential in simulation-based shape design, where high-dimensional parameterizations hinder optimization, surrogate modeling, and systematic design-spac…
Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Hankel dynamic mode decomposition
Giorgio Palma, Andrea Serani, Matteo Diez
In this study, we present and validate an ensemble-based Hankel Dynamic Mode Decomposition with control (HDMDc) for uncertainty-aware seakeeping predictions of a high-speed catamar…
System Identification of a Moored ASV with Recessed Moon Pool via Deterministic and Bayesian Hankel-DMDc
Giorgio Palma, Ivan Santic, Andrea Serani +2
This study addresses the system identification of a small autonomous surface vehicle (ASV) under moored conditions using Hankel dynamic mode decomposition with control (HDMDc) and…
Extending Parametric Model Embedding with Physical Information for Design-space Dimensionality Reduction in Shape Optimization
Andrea Serani, Giorgio Palma, Jeroen Wackers +3
Design-space dimensionality reduction is essential to mitigate the cost of high-fidelity simulation-based optimization, especially when dealing with high-dimensional geometric para…
Model-free system identification of surface ships in waves via Hankel dynamic mode decomposition with control
Giorgio Palma, Andrea Serani, Shawn Aram +3
This study introduces and compares the Hankel dynamic mode decomposition with control (Hankel-DMDc) and a novel Bayesian extension of Hankel-DMDc as model-free (i.e., data-driven a…