Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems

Abstract

This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR). This approach is applied initially to a literature case, the simulation of the stokes problems, and in the following to a real-world industrial problem, inside a shape optimization pipeline for a naval engineering problem.

Cited by in corpus (7)

• Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing
• $\text{FastSVD-ML-ROM}$: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications
• A multi-fidelity approach coupling parameter space reduction and non-intrusive POD with application to structural optimization of passenger ship hulls
• Multi-Physics Model Bias Correction with Data-Driven Reduced Order Modelling Techniques: Application to Nuclear Case Studies
• Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation
• Vanquishing the computational cost of passive gamma emission tomography simulations: a physics-aware reduced order modeling approach
• A Comparison of Parametric Dynamic Mode Decomposition Algorithms for Thermal-Hydraulics Applications