Energy-conserving hyper-reduction and temporal localization for reduced order models of the incompressible Navier-Stokes equations
From MaRDI portal
Publication:6196594
DOI10.1016/j.jcp.2023.112697OpenAlexW4389453886MaRDI QIDQ6196594
Publication date: 14 March 2024
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2023.112697
incompressible Navier-Stokes equationsenergy conservationreduced order modelsdiscrete empirical interpolation methodMahalanobis regularizationtemporal localization
Basic methods in fluid mechanics (76Mxx) Numerical methods for ordinary differential equations (65Lxx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx)
Cites Work
- The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows
- Accuracy analysis of explicit Runge-Kutta methods applied to the incompressible Navier-Stokes equations
- Effort- and flow-constraint reduction methods for structure preserving model reduction of port-Hamiltonian systems
- Reduced-order modelling for flow control. Papers based on the presentations at the course, Udine, Italy, September 2008
- Quantification of uncertainty: improving efficiency and technology. QUIET. Selected contributions based on the presentations at the international workshop, Trieste, Italy, July 18--21, 2017
- A nonlinear Hamiltonian structure for the Euler equations
- Stable Galerkin reduced-order models for linearized compressible flow
- Performance of under-resolved two-dimensional incompressible flow simulations. II
- Jacobian-free Newton-Krylov methods: a survey of approaches and applications.
- Inverses of \(2\times 2\) block matrices
- A user-friendly hybrid sparse matrix class in C++
- Galerkin v. least-squares Petrov-Galerkin projection in nonlinear model reduction
- Energy balance and mass conservation in reduced-order models of fluid flows
- An `empirical interpolation' method: Application to efficient reduced-basis discretization of partial differential equations
- Model reduction for compressible flows using POD and Galerkin projection
- Limitations of the \(L\)-curve method in ill-posed problems
- Structure-preserving model reduction for dynamical systems with a first integral
- Reduced order models for Lagrangian hydrodynamics
- Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods
- Entropy stable reduced order modeling of nonlinear conservation laws
- On the stability of projection-based model order reduction for convection-dominated laminar and turbulent flows
- Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
- Structure-preserving Galerkin POD reduced-order modeling of Hamiltonian systems
- Data-driven operator inference for nonintrusive projection-based model reduction
- Structure-preserving model-reduction of dissipative Hamiltonian systems
- Temporal localized nonlinear model reduction with a priori error estimate
- A stabilized POD model for turbulent flows over a range of Reynolds numbers: optimal parameter sampling and constrained projection
- Energy-conserving Runge-Kutta methods for the incompressible Navier-Stokes equations
- Quadratic approximation manifold for mitigating the Kolmogorov barrier in nonlinear projection-based model order reduction
- Operator inference for non-intrusive model reduction with quadratic manifolds
- An Accelerated Greedy Missing Point Estimation Procedure
- Structure-Preserving Model Reduction for Nonlinear Port-Hamiltonian Systems
- Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations
- A State Space Error Estimate for POD-DEIM Nonlinear Model Reduction
- Dimensional reduction of nonlinear finite element dynamic models with finite rotations and energy-based mesh sampling and weighting for computational efficiency
- Structure-preserving, stability, and accuracy properties of the energy-conserving sampling and weighting method for the hyper reduction of nonlinear finite element dynamic models
- Nonlinear Model Reduction via Discrete Empirical Interpolation
- Symplectic Model Reduction of Hamiltonian Systems
- Efficient reduced-basis treatment of nonaffine and nonlinear partial differential equations
- Study of a staggered fourth-order compact scheme for unsteady incompressible viscous flows
- Turbulence and the dynamics of coherent structures. I. Coherent structures
- Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
- Analysis of Discrete Ill-Posed Problems by Means of the L-Curve
- A derivative-free line search and global convergence of Broyden-like method for nonlinear equations
- Data-Driven Filtered Reduced Order Modeling of Fluid Flows
- Stabilization of projection‐based reduced‐order models
- Missing Point Estimation in Models Described by Proper Orthogonal Decomposition
- Model Order Reduction in Fluid Dynamics: Challenges and Perspectives
- Structure-preserving reduced basis methods for Poisson systems
- Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns
- Data-Driven Science and Engineering
- Randomized Discrete Empirical Interpolation Method for Nonlinear Model Reduction
- Stability of Discrete Empirical Interpolation and Gappy Proper Orthogonal Decomposition with Randomized and Deterministic Sampling Points
- Conservative Model Order Reduction for Fluid Flow
- Low-dimensional modelling of high-Reynolds-number shear flows incorporating constraints from the Navier–Stokes equation
- Relaxation Runge--Kutta Methods: Conservation and Stability for Inner-Product Norms
- Relaxation Runge--Kutta Methods: Fully Discrete Explicit Entropy-Stable Schemes for the Compressible Euler and Navier--Stokes Equations
- Constrained sparse Galerkin regression
- Turbulence, Coherent Structures, Dynamical Systems and Symmetry
- Localized Discrete Empirical Interpolation Method
- The elements of statistical learning. Data mining, inference, and prediction
- Gradient-Preserving Hyper-Reduction of Nonlinear Dynamical Systems via Discrete Empirical Interpolation
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
