Operator inference with roll outs for learning reduced models from scarce and low-quality data
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Publication:6135185
DOI10.1016/j.camwa.2023.06.012arXiv2212.01418OpenAlexW4382360951MaRDI QIDQ6135185
Dirk Hartmann, Benjamin Peherstorfer, Wayne Isaac Tan Uy
Publication date: 23 August 2023
Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2212.01418
System structure simplification (93B11) Operator-theoretic methods (93B28) Control/observation systems governed by ordinary differential equations (93C15)
Cites Work
- Unnamed Item
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs
- A data-driven approximation of the koopman operator: extending dynamic mode decomposition
- Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Application to transport and continuum mechanics.
- A framework for the solution of the generalized realization problem
- Non-intrusive reduced order modeling of nonlinear problems using neural networks
- Reduced order modeling for nonlinear structural analysis using Gaussian process regression
- Data-driven reduced order modeling for time-dependent problems
- Data-driven variational multiscale reduced order models
- Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
- Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories
- Non-intrusive data-driven model reduction for differential-algebraic equations derived from lifting transformations
- Lift \& learn: physics-informed machine learning for large-scale nonlinear dynamical systems
- Neural-network learning of SPOD latent dynamics
- An artificial neural network framework for reduced order modeling of transient flows
- Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
- Data-driven operator inference for nonintrusive projection-based model reduction
- On dynamic mode decomposition: theory and applications
- Hamiltonian operator inference: physics-preserving learning of reduced-order models for canonical Hamiltonian systems
- Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference
- Bayesian operator inference for data-driven reduced-order modeling
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
- Model Reduction of Bilinear Systems in the Loewner Framework
- Tangential interpolation-based eigensystem realization algorithm for MIMO systems
- Data-Driven Parametrized Model Reduction in the Loewner Framework
- Dynamic mode decomposition of numerical and experimental data
- An Online Method for Interpolating Linear Parametric Reduced-Order Models
- Data‐driven model order reduction of quadratic‐bilinear systems
- Vector Fitting for Matrix-valued Rational Approximation
- Spectral analysis of nonlinear flows
- Interpolation among reduced‐order matrices to obtain parameterized models for design, optimization and probabilistic analysis
- A method for interpolating on manifolds structural dynamics reduced-order models
- An eigensystem realization algorithm for modal parameter identification and model reduction
- On the Scalar Rational Interpolation Problem
- Extracting Sparse High-Dimensional Dynamics from Limited Data
- Data-Driven Filtered Reduced Order Modeling of Fluid Flows
- Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
- Surface quasi-geostrophic dynamics
- Stability Domains for Quadratic-Bilinear Reduced-Order Models
- Kernel Analog Forecasting: Multiscale Test Problems
- Learning Low-Dimensional Dynamical-System Models from Noisy Frequency-Response Data with Loewner Rational Interpolation
- Efficient geometrical parametrisation techniques of interfaces for reduced-order modelling: application to fluid–structure interaction coupling problems
- Breaking the Kolmogorov Barrier with Nonlinear Model Reduction
- Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference
- Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations
- Bayesian differential programming for robust systems identification under uncertainty
- Sparse dynamics for partial differential equations
- Data-Driven Reduced Model Construction with Time-Domain Loewner Models
- Approximation of Large-Scale Dynamical Systems
- Active Operator Inference for Learning Low-Dimensional Dynamical-System Models from Noisy Data
