A multifidelity deep operator network approach to closure for multiscale systems
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Publication:6116145
DOI10.1016/j.cma.2023.116161arXiv2303.08893OpenAlexW4381548105MaRDI QIDQ6116145
Publication date: 11 August 2023
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2303.08893
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Cites Work
- Comparative numerical analysis using reduced-order modeling strategies for nonlinear large-scale systems
- The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows
- Artificial viscosity proper orthogonal decomposition
- Minimal subspace rotation on the Stiefel manifold for stabilization and enhancement of projection-based reduced order models for the compressible Navier-Stokes equations
- Problem reduction, renormalization, and memory
- Stable Galerkin reduced-order models for linearized compressible flow
- Spatio-temporal symmetries and bifurcations via bi-orthogonal decompositions
- A spectral viscosity method for correcting the long-term behavior of POD models.
- Stability properties of POD-Galerkin approximations for the compressible Navier-Stokes equations
- Galerkin v. least-squares Petrov-Galerkin projection in nonlinear model reduction
- Neural network closures for nonlinear model order reduction
- Data-driven variational multiscale reduced order models
- Frame invariant neural network closures for Kraichnan turbulence
- Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems
- On the stability of projection-based model order reduction for convection-dominated laminar and turbulent flows
- Windowed least-squares model reduction for dynamical systems
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
- Stable \textit{a posteriori} LES of 2D turbulence using convolutional neural networks: backscattering analysis and generalization to higher \(Re\) via transfer learning
- Improved architectures and training algorithms for deep operator networks
- Real-time reduced-order modeling of stochastic partial differential equations via time-dependent subspaces
- On-the-fly reduced order modeling of passive and reactive species via time-dependent manifolds
- Windowed space-time least-squares Petrov-Galerkin model order reduction for nonlinear dynamical systems
- Approximate deconvolution reduced order modeling
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations
- The real butterfly effect
- An Ensemble-Proper Orthogonal Decomposition Method for the Nonstationary Navier--Stokes Equations
- The dynamics of coherent structures in the wall region of a turbulent boundary layer
- Intermodal energy transfers in a proper orthogonal decomposition–Galerkin representation of a turbulent separated flow
- Data-Driven Discovery of Closure Models
- Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons
- Error estimates for DeepONets: a deep learning framework in infinite dimensions
- Model Reduction for Transport-Dominated Problems via Online Adaptive Bases and Adaptive Sampling
- Low-dimensional modelling of high-Reynolds-number shear flows incorporating constraints from the Navier–Stokes equation
- Turbulence, Coherent Structures, Dynamical Systems and Symmetry
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