39 citations · 41 across the 3 of their papers we have counts for
3 papers
physics.flu-dyn2026
ShipNet: A Geometric Deep Learning Surrogate for Real-Time Ship Hydrodynamics
Kirsten Odendaal, George Drakoulas
Accurate prediction of hydrodynamic performance is central to ship design, yet high-fidelity computational fluid dynamics remains prohibitively expensive for large-scale parametric…
cs.LG2023★ 2 cited
Physics-based Reduced Order Modeling for Uncertainty Quantification of Guided Wave Propagation using Bayesian Optimization
G. I. Drakoulas, T. V. Gortsas, D. Polyzos
In the context of digital twins, structural health monitoring (SHM) constitutes the backbone of condition-based maintenance, facilitating the interconnection between virtual and ph…
cs.LG2022★ 39 cited
: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications
G. I. Drakoulas, T. V. Gortsas, G. C. Bourantas +2
Digital twins have emerged as a key technology for optimizing the performance of engineering products and systems. High-fidelity numerical simulations constitute the backbone of en…