Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem
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Publication:2214654
DOI10.1016/j.jcp.2019.01.031zbMath1459.76117OpenAlexW2809491586WikidataQ128345010 ScholiaQ128345010MaRDI QIDQ2214654
Deep Ray, Qian Wang, Jan S. Hesthaven
Publication date: 10 December 2020
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: http://infoscience.epfl.ch/record/255708
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Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Reduced order methods for modeling and computational reduction
- Calibrated reduced-order POD-Galerkin system for fluid flow modelling
- A nonintrusive reduced basis method applied to aeroacoustic simulations
- Reduced-order fluid/structure modeling of a complete aircraft configuration
- Stable Galerkin reduced-order models for linearized compressible flow
- Efficient implementation of essentially nonoscillatory shock-capturing schemes
- A survey of several finite difference methods for systems of nonlinear hyperbolic conservation laws
- Stability properties of POD-Galerkin approximations for the compressible Navier-Stokes equations
- Non-intrusive reduced order modeling of nonlinear problems using neural networks
- An `empirical interpolation' method: Application to efficient reduced-basis discretization of partial differential equations
- Efficient implementation of weighted ENO schemes
- High dimensional polynomial interpolation on sparse grids
- Reduced order modeling for nonlinear structural analysis using Gaussian process regression
- Efficient model reduction of parametrized systems by matrix discrete empirical interpolation
- Compact high order finite volume method on unstructured grids I: Basic formulations and one-dimensional schemes
- Certified reduced basis approximation for parametrized partial differential equations and applications
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
- A prioriconvergence of the Greedy algorithm for the parametrized reduced basis method
- PROPER ORTHOGONAL DECOMPOSITION AND ITS APPLICATIONS—PART I: THEORY
- Certified Reduced Basis Methods for Parametrized Partial Differential Equations
- Supremizer stabilization of POD-Galerkin approximation of parametrized steady incompressible Navier-Stokes equations
- Nonlinear Model Reduction via Discrete Empirical Interpolation
- Convergence Rates for Greedy Algorithms in Reduced Basis Methods
- TVD schemes for open channel flow
- Reduced-Order Modeling and ROM-Based Optimization of Batch Chromatography
- Efficient greedy algorithms for high-dimensional parameter spaces with applications to empirical interpolation and reduced basis methods
- Localized Discrete Empirical Interpolation Method
- Reduced basis method for finite volume approximations of parametrized linear evolution equations