Learning physics-based reduced-order models from data using nonlinear manifolds
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Publication:6552123
DOI10.1063/5.0170105zbMATH Open1545.3707MaRDI QIDQ6552123
Stephen J. Wright, Rudy J. M. Geelen, Laura Balzano, Karen Willcox
Publication date: 8 June 2024
Published in: Chaos (Search for Journal in Brave)
Cites Work
- Unnamed Item
- A reduced order method for Allen-Cahn equations
- Automated solution of differential equations by the finite element method. The FEniCS book
- Approximate inertial manifolds for the Kuramoto-Sivashinsky equation: Analysis and computations
- Integral manifolds and inertial manifolds for dissipative partial differential equations
- Two-dimensional invariant manifolds and global bifurcations: Some approximation and visualization studies
- POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
- Lift \& learn: physics-informed machine learning for large-scale nonlinear dynamical systems
- A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
- Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
- Data-driven operator inference for nonintrusive projection-based model reduction
- A generalized solution of the orthogonal Procrustes problem
- Über die beste Annäherung von Funktionen einer gegebenen Funktionenklasse
- Hamiltonian operator inference: physics-preserving learning of reduced-order models for canonical Hamiltonian systems
- 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
- Nonlinear Model Reduction via Discrete Empirical Interpolation
- Some global dynamical properties of a class of pattern formation equations
- Dynamic mode decomposition of numerical and experimental data
- Spectral analysis of nonlinear flows
- Inertial Manifolds for Reaction Diffusion Equations in Higher Space Dimensions
- Turbulence and the dynamics of coherent structures. I. Coherent structures
- Low-dimensional models for complex geometry flows: Application to grooved channels and circular cylinders
- Turbulence, Coherent Structures, Dynamical Systems and Symmetry
- Eigenmode analysis in unsteady aerodynamics - Reduced-order models
- A hierarchy of low-dimensional models for the transient and post-transient cylinder wake
- Data-Driven Discovery of Closure Models
- Certified real‐time solution of the parametrized steady incompressible Navier–Stokes equations: rigorous reduced‐basis a posteriori error bounds
- Analysis of Fluid Flows via Spectral Properties of the Koopman Operator
- 9 From the POD-Galerkin method to sparse manifold models
- Dynamic Mode Decomposition and Its Variants
- Two-Sided Projection Methods for Nonlinear Model Order Reduction
- MODEL REDUCTION FOR FLUIDS, USING BALANCED PROPER ORTHOGONAL DECOMPOSITION
- Reduced Basis Methods for Partial Differential Equations
- Coupling of substructures for dynamic analyses.
- Learning physics-based models from data: perspectives from inverse problems and model reduction
- The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent
- Operator inference with roll outs for learning reduced models from scarce and low-quality data
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