Pages that link to "Item:Q2024169"
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The following pages link to An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques (Q2024169):
Displaying 7 items.
- Data-driven reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation (Q2059122) (← links)
- Efficient acoustic topology optimization with the multifrequency quasi-static Ritz vector (MQSRV) method (Q2086057) (← links)
- A Gaussian process regression approach within a data-driven POD framework for engineering problems in fluid dynamics (Q2167597) (← links)
- A Dynamic Mode Decomposition Extension for the Forecasting of Parametric Dynamical Systems (Q6076407) (← links)
- On modal decomposition as surrogate for charge-conservative EHD modelling of Taylor cone jets (Q6085400) (← links)
- Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation (Q6159004) (← links)
- A shape optimization pipeline for marine propellers by means of reduced order modeling techniques (Q6499905) (← links)
