Pages that link to "Item:Q2167597"

The following pages link to A Gaussian process regression approach within a data-driven POD framework for engineering problems in fluid dynamics (Q2167597):

Displaying 8 items.

• A new class of high-order methods for fluid dynamics simulations using Gaussian process modeling: one-dimensional case (Q1668731) (← links)
• Projection-based model reduction: formulations for physics-based machine learning (Q1739759) (← links)
• Reduced basis methods for time-dependent problems (Q5887836) (← links)
• A data-driven surrogate modeling approach for time-dependent incompressible Navier-Stokes equations with dynamic mode decomposition and manifold interpolation (Q6038828) (← links)
• An Adaptive Non-Intrusive Multi-Fidelity Reduced Basis Method for Parameterized Partial Differential Equations (Q6110109) (← links)
• \textit{FastSVD-ML-ROM}: a reduced-order modeling framework based on machine learning for real-time applications (Q6116133) (← links)
• Deep convolutional Ritz method: parametric PDE surrogates without labeled data (Q6132294) (← links)
• A physics-based reduced order model for urban air pollution prediction (Q6153871) (← links)