9 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…
A Survey on Design-space Dimensionality Reduction Methods for Shape Optimization
Andrea Serani, Matteo Diez
The rapidly evolving field of engineering design of functional surfaces necessitates sophisticated tools to manage the inherent complexity of high-dimensional design spaces. This s…