Koopman dynamic-oriented deep learning for invariant subspace identification and full-state prediction of complex systems

Numerical methods for initial value problems involving ordinary differential equations (65L05) Computational methods for invariant manifolds of dynamical systems (37M21) Computational methods for attractors of dynamical systems (37M22)

Cites Work

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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
A kernel-based method for data-driven Koopman spectral analysis
Data-driven model reduction and transfer operator approximation
A data-driven approximation of the koopman operator: extending dynamic mode decomposition
A fast immersed boundary method using a nullspace approach and multi-domain far-field boundary conditions
Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
Deep learning nonlinear multiscale dynamic problems using Koopman operator
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
A review on deep reinforcement learning for fluid mechanics
Machine learning closures for model order reduction of thermal fluids
A parameterized non-intrusive reduced order model and error analysis for general time-dependent nonlinear partial differential equations and its applications
The immersed boundary method: a projection approach
Non-intrusive reduced order modelling of fluid-structure interactions
Spectral properties of dynamical systems, model reduction and decompositions
On dynamic mode decomposition: theory and applications
Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes
A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence
A POD reduced-order model for eigenvalue problems with application to reactor physics
Model Reduction for Flow Analysis and Control
Dynamic mode decomposition of numerical and experimental data
Spectral analysis of nonlinear flows
A hierarchy of low-dimensional models for the transient and post-transient cylinder wake
Hamiltonian Systems and Transformation in Hilbert Space
Sparse reduced-order modelling: sensor-based dynamics to full-state estimation
Turbulence statistics in fully developed channel flow at low Reynolds number
Analysis of Fluid Flows via Spectral Properties of the Koopman Operator
Dynamic Mode Decomposition and Its Variants
Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability
Using machine learning to detect the turbulent region in flow past a circular cylinder
Sparse identification of nonlinear dynamics with low-dimensionalized flow representations
Turbulence Modeling in the Age of Data
Linearly Recurrent Autoencoder Networks for Learning Dynamics
Deep learning enhanced dynamic mode decomposition

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