Data-driven reduced-order modeling of spatiotemporal chaos with neural ordinary differential equations
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Data-driven reduced-order modeling of spatiotemporal chaos with neural ordinary differential equations
Authors
Keywords
-
Journal
CHAOS
Volume 32, Issue 7, Pages 073110
Publisher
AIP Publishing
Online
2022-07-09
DOI
10.1063/5.0069536
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Revealing the state space of turbulence using machine learning
- (2021) Jacob Page et al. Physical Review Fluids
- Discovering Physical Concepts with Neural Networks
- (2020) Raban Iten et al. PHYSICAL REVIEW LETTERS
- Structured light entities, chaos and nonlocal maps
- (2020) A. Yu Okulov CHAOS SOLITONS & FRACTALS
- Time-series learning of latent-space dynamics for reduced-order model closure
- (2020) Romit Maulik et al. PHYSICA D-NONLINEAR PHENOMENA
- Leveraging reduced-order models for state estimation using deep learning
- (2020) Nirmal J. Nair et al. JOURNAL OF FLUID MECHANICS
- Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics
- (2020) P.R. Vlachas et al. NEURAL NETWORKS
- Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
- (2020) Kai Fukami et al. PHYSICS OF FLUIDS
- A novel method of low-dimensional representation for temporal behavior of flow fields using deep autoencoder
- (2019) Noriyasu Omata et al. AIP Advances
- LYAPUNOV EXPONENTS OF THE KURAMOTO–SIVASHINSKY PDE
- (2019) RUSSELL A. EDSON et al. ANZIAM JOURNAL
- Data-driven discovery of coordinates and governing equations
- (2019) Kathleen Champion et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
- (2018) Jaideep Pathak et al. PHYSICAL REVIEW LETTERS
- Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
- (2018) Pantelis R. Vlachas et al. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Rapid time series prediction with a hardware-based reservoir computer
- (2018) Daniel Canaday et al. CHAOS
- Deep learning for universal linear embeddings of nonlinear dynamics
- (2018) Bethany Lusch et al. Nature Communications
- Estimating the Dimension of an Inertial Manifold from Unstable Periodic Orbits
- (2016) X. Ding et al. PHYSICAL REVIEW LETTERS
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- (2016) Steven L. Brunton et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Reduction of SO(2) Symmetry for Spatially Extended Dynamical Systems
- (2015) Nazmi Burak Budanur et al. PHYSICAL REVIEW LETTERS
- Inertial manifolds and finite-dimensional reduction for dissipative PDEs
- (2014) Sergey Zelik PROCEEDINGS OF THE ROYAL SOCIETY OF EDINBURGH SECTION A-MATHEMATICS
- Applied Koopmanism
- (2012) Marko Budišić et al. CHAOS
- Geometry of Inertial Manifolds Probed via a Lyapunov Projection Method
- (2012) Hong-liu Yang et al. PHYSICAL REVIEW LETTERS
- Nonlinear dimensionality reduction in molecular simulation: The diffusion map approach
- (2011) Andrew L. Ferguson et al. CHEMICAL PHYSICS LETTERS
- On the State Space Geometry of the Kuramoto–Sivashinsky Flow in a Periodic Domain
- (2010) Predrag Cvitanović et al. SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS
- Hyperbolicity and the Effective Dimension of Spatially Extended Dissipative Systems
- (2009) Hong-liu Yang et al. PHYSICAL REVIEW LETTERS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started