3 citations · 5 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
Bayesian dynamic mode decomposition for real-time ship motion digital twinning
Giorgio Palma, Andrea Serani, Kevin McTaggart +4
Digital twins are widely considered enablers of groundbreaking changes in the development, operation, and maintenance of novel generations of products. They are meant to provide re…
Analysis, forecasting and system identification of a floating offshore wind turbine using dynamic mode decomposition
Giorgio Palma, Andrea Bardazzi, Alessia Lucarelli +4
This article presents the data-driven equation-free modeling of the dynamics of a hexafloat floating offshore wind turbine based on the application of dynamic mode decomposition (D…
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…
Democratizing Uncertainty Quantification
Linus Seelinger, Anne Reinarz, Mikkel B. Lykkegaard +22
Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In thi…