An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques
From MaRDI portal
Publication:2024169
DOI10.1007/s40574-020-00263-4OpenAlexW3097806851MaRDI QIDQ2024169
Gianpiero Lavini, Gianluca Gustin, Nicola Demo, Gianluigi Rozza, Giulio Ortali
Publication date: 3 May 2021
Published in: Bollettino dell'Unione Matematica Italiana (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2004.11201
finite volume methodshape optimizationcomputational fluid dynamicsfree form deformationdata-driven reduced order modeling
Related Items
A Gaussian process regression approach within a data-driven POD framework for engineering problems in fluid dynamics, A Dynamic Mode Decomposition Extension for the Forecasting of Parametric Dynamical Systems, On modal decomposition as surrogate for charge-conservative EHD modelling of Taylor cone jets, Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation, Data-driven reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation, Efficient acoustic topology optimization with the multifrequency quasi-static Ritz vector (MQSRV) method
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Shape optimization by free-form deformation: existence results and numerical solution for Stokes flows
- Parametric free-form shape design with PDE models and reduced basis method
- Data-driven POD-Galerkin reduced order model for turbulent flows
- Volume of fluid (VOF) method for the dynamics of free boundaries
- Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier-Stokes equations
- Non-intrusive reduced order modeling of nonlinear problems using neural networks
- Reduced order modeling for nonlinear structural analysis using Gaussian process regression
- Certified PDE-constrained parameter optimization using reduced basis surrogate models for evolution problems
- On dynamic mode decomposition: theory and applications
- Certified Reduced Basis Methods for Parametrized Partial Differential Equations
- Progressive construction of a parametric reduced‐order model for PDE‐constrained optimization
- Dynamic mode decomposition of numerical and experimental data
- Hamiltonian Systems and Transformation in Hilbert Space
- Reduced-Order Semi-Implicit Schemes for Fluid-Structure Interaction Problems
- Free-form deformation, mesh morphing and reduced-order methods: enablers for efficient aerodynamic shape optimisation
- Genetic Algorithms and the Optimal Allocation of Trials