Pages that link to "Item:Q2815667"
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The following pages link to Proper orthogonal decomposition closure models for fluid flows: Burgers equation (Q2815667):
Displaying 29 items.
- Mathematical and numerical results on the sensitivity of the POD approximation relative to the Burgers equation (Q297847) (← links)
- Subgrid-scale closure for the inviscid Burgers-Hopf equation (Q390097) (← links)
- Artificial viscosity proper orthogonal decomposition (Q534837) (← links)
- Two-level discretizations of nonlinear closure models for proper orthogonal decomposition (Q613986) (← links)
- Proper orthogonal decomposition closure models for turbulent flows: a numerical comparison (Q695943) (← links)
- A stabilized proper orthogonal decomposition reduced-order model for large scale quasigeostrophic ocean circulation (Q904252) (← links)
- Proper orthogonal decomposition and incompressible flow: an application to particle modeling (Q993869) (← links)
- Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models (Q1686574) (← links)
- Stabilized principal interval decomposition method for model reduction of nonlinear convective systems with moving shocks (Q1715737) (← links)
- Neural network closures for nonlinear model order reduction (Q1756917) (← links)
- Data-driven nonintrusive reduced order modeling for dynamical systems with moving boundaries using Gaussian process regression (Q2020804) (← links)
- Reinforcement learning-based model reduction for partial differential equations: application to the Burgers equation (Q2094039) (← links)
- Calibration of projection-based reduced-order models for unsteady compressible flows (Q2120780) (← links)
- A long short-term memory embedding for hybrid uplifted reduced order models (Q2125587) (← links)
- Windowed least-squares model reduction for dynamical systems (Q2127002) (← links)
- The adjoint Petrov-Galerkin method for non-linear model reduction (Q2184304) (← links)
- An artificial neural network framework for reduced order modeling of transient flows (Q2206568) (← links)
- POD-(H)DG method for incompressible flow simulations (Q2210653) (← links)
- A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction (Q2245302) (← links)
- Data-driven closure of projection-based reduced order models for unsteady compressible flows (Q2246325) (← links)
- Machine learning closures for model order reduction of thermal fluids (Q2295965) (← links)
- POD-Galerkin method for finite volume approximation of Navier-Stokes and RANS equations (Q2308660) (← links)
- Reduced-order model for the BGK equation based on POD and optimal transport (Q2311642) (← links)
- Reduced collocation method for time-dependent parametrized partial differential equations (Q2330394) (← links)
- A stabilized POD model for turbulent flows over a range of Reynolds numbers: optimal parameter sampling and constrained projection (Q2425266) (← links)
- Krylov-subspace recycling via the POD-augmented conjugate-gradient method (Q2827063) (← links)
- Data-Driven Filtered Reduced Order Modeling of Fluid Flows (Q4568096) (← links)
- Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling (Q6097872) (← links)
- An adaptive, training-free reduced-order model for convection-dominated problems based on hybrid snapshots (Q6574153) (← links)
