Pages that link to "Item:Q781977"

The following pages link to Data-driven POD-Galerkin reduced order model for turbulent flows (Q781977):

Displaying 50 items.

• Generalized HWD-POD method and coupling low-dimensional dynamical system of turbulence (Q481231) (← links)
• Artificial viscosity proper orthogonal decomposition (Q534837) (← links)
• A POD-Galerkin reduced order model of a turbulent convective buoyant flow of Sodium over a backward-facing step (Q822069) (← links)
• Reduced-order modeling of turbulent forced convection with parametric conditions (Q865845) (← links)
• A domain decomposition non-intrusive reduced order model for turbulent flows (Q1735013) (← links)
• Data-driven variational multiscale reduced order models (Q2020754) (← links)
• An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques (Q2024169) (← links)
• Data-driven reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation (Q2059122) (← links)
• Stabilized reduced-order models for unsteady incompressible flows in three-dimensional parametrized domains (Q2084084) (← links)
• Error analysis of a residual-based stabilization-motivated POD-ROM for incompressible flows (Q2096854) (← links)
• Verifiability of the data-driven variational multiscale reduced order model (Q2103423) (← links)
• Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods (Q2123750) (← links)
• A POD-Galerkin reduced order model for a LES filtering approach (Q2131062) (← links)
• Parametric solutions of turbulent incompressible flows in OpenFOAM via the proper generalised decomposition (Q2136490) (← links)
• The neural network shifted-proper orthogonal decomposition: a machine learning approach for non-linear reduction of hyperbolic equations (Q2138717) (← links)
• Unsteady physics-based reduced order modeling for large-scale compressible aerodynamic applications (Q2139580) (← links)
• A Gaussian process regression approach within a data-driven POD framework for engineering problems in fluid dynamics (Q2167597) (← links)
• Neural-network learning of SPOD latent dynamics (Q2168295) (← links)
• Data-driven reduced order model with temporal convolutional neural network (Q2175300) (← links)
• A few techniques to improve data-driven reduced-order simulations for unsteady flows (Q2176856) (← links)
• Reducing data-driven dynamical subgrid scale models by physical constraints (Q2176859) (← links)
• A framework to develop data-driven turbulence models for flows with organised unsteadiness (Q2214628) (← links)
• Parametric non-intrusive model order reduction for flow-fields using unsupervised machine learning (Q2237497) (← links)
• Reduced order modelling for a rotor-stator cavity using proper orthogonal decomposition (Q2245198) (← links)
• On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis (Q2245201) (← links)
• Data-driven closure of projection-based reduced order models for unsteady compressible flows (Q2246325) (← links)
• Nonlinear closure modeling in reduced order models for turbulent flows: a dynamical system approach (Q2296406) (← links)
• A reduced order variational multiscale approach for turbulent flows (Q2305534) (← links)
• POD-Galerkin method for finite volume approximation of Navier-Stokes and RANS equations (Q2308660) (← links)
• A stabilized POD model for turbulent flows over a range of Reynolds numbers: optimal parameter sampling and constrained projection (Q2425266) (← links)
• Reduced-basis modeling of turbulent plane channel flow (Q2508840) (← links)
• A hybrid reduced order method for modelling turbulent heat transfer problems (Q2664048) (← links)
• Operator inference and physics-informed learning of low-dimensional models for incompressible flows (Q2672189) (← links)
• Pressure data-driven variational multiscale reduced order models (Q2681113) (← links)
• A new approach to proper orthogonal decomposition with difference quotients (Q2692798) (← links)
• Hybrid data-driven closure strategies for reduced order modeling (Q2698238) (← links)
• Reduced order modeling for parametrized generalized Newtonian fluid flows (Q2699383) (← links)
• A data-driven model based on modal decomposition: application to the turbulent channel flow over an anisotropic porous wall (Q3390383) (← links)
• MODEL REDUCTION OF TURBULENT FLUID FLOWS USING THE SUPPLY RATE (Q3394419) (← links)
• Data-Driven Filtered Reduced Order Modeling of Fluid Flows (Q4568096) (← links)
• A Review on Reduced Order Modeling using DMD-Based Methods (Q4973303) (← links)
• On Optimal Pointwise in Time Error Bounds and Difference Quotients for the Proper Orthogonal Decomposition (Q5009343) (← links)
• Non-intrusive Polynomial Chaos Method Applied to Full-Order and Reduced Problems in Computational Fluid Dynamics: A Comparison and Perspectives (Q5141296) (← links)
• Error Analysis of Proper Orthogonal Decomposition Stabilized Methods for Incompressible Flows (Q5151925) (← links)
• Quantifying Truncation-Related Uncertainties in Unsteady Fluid Dynamics Reduced Order Models (Q5158919) (← links)
• Data-driven construction of a reduced-order model for supersonic boundary layer transition (Q5229773) (← links)
• Reduced basis methods for time-dependent problems (Q5887836) (← links)
• A two-stage deep learning architecture for model reduction of parametric time-dependent problems (Q6048996) (← links)
• A non‐linear non‐intrusive reduced order model of fluid flow by auto‐encoder and self‐attention deep learning methods (Q6060947) (← links)
• Simulation of the interaction of light with <scp>3‐D</scp> metallic nanostructures using a proper orthogonal decomposition‐Galerkin reduced‐order discontinuous Galerkin time‐domain method (Q6064500) (← links)