Learning chaotic systems from noisy data via multi-step optimization and adaptive training
From MaRDI portal
Publication:6571528
DOI10.1063/5.0114542MaRDI QIDQ6571528
Lei Zhang, Shao-Qiang Tang, Guo-wei He
Publication date: 12 July 2024
Published in: Chaos (Search for Journal in Brave)
Cites Work
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- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Poincaré maps for multiscale physics discovery and nonlinear Floquet theory
- DLGA-PDE: discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Nonlinear System Identification
- A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems
- Dynamic mode decomposition of numerical and experimental data
- Transition in shear flows. Nonlinear normality versus non-normal linearity
- Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
- Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator
- Hydrodynamic Stability and Turbulence: Beyond Transients to a Self‐Sustaining Process
- Nonlinear System Identification
- DL-PDE: Deep-Learning Based Data-Driven Discovery of Partial Differential Equations from Discrete and Noisy Data
- How entropic regression beats the outliers problem in nonlinear system identification
- Knowledge-based learning of nonlinear dynamics and chaos
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