Trans-Bifurcation Prediction of Dynamics in terms of Extreme Learning Machines with Control Inputs
Fuente:
arXiv
Saved in:
| Main Authors: | Tadokoro, Satoru, Yamaguchi, Akihiro, Namiki, Takao, Tsuda, Ichiro |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Dynamics-Informed Deep Learning for Predicting Extreme Events
by: Katsidoniotaki, Eirini, et al.
Published: (2026)
by: Katsidoniotaki, Eirini, et al.
Published: (2026)
Incorporating Coupling Knowledge into Echo State Networks for Learning Spatiotemporally Chaotic Dynamics
by: Chu, Kuei-Jan, et al.
Published: (2025)
by: Chu, Kuei-Jan, et al.
Published: (2025)
Prediction of Unobserved Bifurcation by Unsupervised Extraction of Slowly Time-Varying System Parameter Dynamics from Time Series Using Reservoir Computing
by: Tokuda, Keita, et al.
Published: (2024)
by: Tokuda, Keita, et al.
Published: (2024)
Machine Learning for Predicting Chaotic Systems
by: Schötz, Christof, et al.
Published: (2024)
by: Schötz, Christof, et al.
Published: (2024)
Deep Learning for Prediction and Classifying the Dynamical behaviour of Piecewise Smooth Maps
by: S, Vismaya V, et al.
Published: (2024)
by: S, Vismaya V, et al.
Published: (2024)
Controlling Dynamical Systems into Unseen Target States Using Machine Learning
by: Köglmayr, Daniel, et al.
Published: (2024)
by: Köglmayr, Daniel, et al.
Published: (2024)
Machine-Precision Prediction of Low-Dimensional Chaotic Systems
by: Schötz, Christof, et al.
Published: (2025)
by: Schötz, Christof, et al.
Published: (2025)
Active search for Bifurcations
by: Psarellis, Yorgos M., et al.
Published: (2024)
by: Psarellis, Yorgos M., et al.
Published: (2024)
Predicting Chaotic System Behavior using Machine Learning Techniques
by: Rao, Huaiyuan, et al.
Published: (2024)
by: Rao, Huaiyuan, et al.
Published: (2024)
Minimal Deterministic Echo State Networks Outperform Random Reservoirs in Learning Chaotic Dynamics
by: Martinuzzi, Francesco
Published: (2025)
by: Martinuzzi, Francesco
Published: (2025)
Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems
by: Shokar, Ira J. S., et al.
Published: (2025)
by: Shokar, Ira J. S., et al.
Published: (2025)
Using Machine Learning and Neural Networks to Analyze and Predict Chaos in Multi-Pendulum and Chaotic Systems
by: Ramachandruni, Vasista, et al.
Published: (2025)
by: Ramachandruni, Vasista, et al.
Published: (2025)
Comparative Analysis of Predicting Subsequent Steps in Hénon Map
by: S, Vismaya V, et al.
Published: (2024)
by: S, Vismaya V, et al.
Published: (2024)
GEN2: A Generative Prediction-Correction Framework for Long-time Emulations of Spatially-Resolved Climate Extremes
by: Wang, Mengze, et al.
Published: (2025)
by: Wang, Mengze, et al.
Published: (2025)
Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network
by: Rostamijavanani, Abdolvahhab, et al.
Published: (2024)
by: Rostamijavanani, Abdolvahhab, et al.
Published: (2024)
Constants of Motion for Conserved and Non-conserved Dynamics
by: Zimmer, Michael F.
Published: (2024)
by: Zimmer, Michael F.
Published: (2024)
Characterizing Nonlinear Dynamics via Smooth Prototype Equivalences
by: Friedman, Roy, et al.
Published: (2025)
by: Friedman, Roy, et al.
Published: (2025)
Conditional Score-Based Modeling of Effective Langevin Dynamics
by: Giorgini, Ludovico T.
Published: (2026)
by: Giorgini, Ludovico T.
Published: (2026)
A Numerical Study of Chaotic Dynamics of K-S Equation with FNOs
by: Khetrapal, Surbhi, et al.
Published: (2024)
by: Khetrapal, Surbhi, et al.
Published: (2024)
Network Dynamics-Based Framework for Understanding Deep Neural Networks
by: Lin, Yuchen, et al.
Published: (2025)
by: Lin, Yuchen, et al.
Published: (2025)
Backpropagation on Dynamical Networks
by: Tan, Eugene, et al.
Published: (2022)
by: Tan, Eugene, et al.
Published: (2022)
Predicting Forced Responses of Probability Distributions via the Fluctuation-Dissipation Theorem and Generative Modeling
by: Giorgini, Ludovico T., et al.
Published: (2025)
by: Giorgini, Ludovico T., et al.
Published: (2025)
Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
by: Bishnoi, Suresh, et al.
Published: (2022)
by: Bishnoi, Suresh, et al.
Published: (2022)
Predicting two-dimensional spatiotemporal chaotic patterns with optimized high-dimensional hybrid reservoir computing
by: Nakano, Tamon, et al.
Published: (2025)
by: Nakano, Tamon, et al.
Published: (2025)
Identifying Stochastic Dynamics from Non-Sequential Data (DyNoSeD)
by: Lu, Zhixin, et al.
Published: (2025)
by: Lu, Zhixin, et al.
Published: (2025)
Learning with Mandelbrot and Julia
by: Tjahjono, V. R., et al.
Published: (2025)
by: Tjahjono, V. R., et al.
Published: (2025)
Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
by: Brenner, Manuel, et al.
Published: (2022)
by: Brenner, Manuel, et al.
Published: (2022)
DyMixOp: A Neural Operator Designed from a Complex Dynamics Perspective with Local-Global Mixing for Solving PDEs
by: Lai, Pengyu, et al.
Published: (2025)
by: Lai, Pengyu, et al.
Published: (2025)
Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems
by: Chakraborty, Dibyajyoti, et al.
Published: (2026)
by: Chakraborty, Dibyajyoti, et al.
Published: (2026)
PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systems
by: Farkane, Ayoub, et al.
Published: (2025)
by: Farkane, Ayoub, et al.
Published: (2025)
CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting
by: Chen, Chuanqi, et al.
Published: (2023)
by: Chen, Chuanqi, et al.
Published: (2023)
Experimental Acquisition and Verification of Spectral Signatures of Dynamic Bifurcations
by: Maity, Suvradip, et al.
Published: (2026)
by: Maity, Suvradip, et al.
Published: (2026)
Prediction Beyond the Medium Range with an Atmosphere-Ocean Model that Combines Physics-based Modeling and Machine Learning
by: Patel, Dhruvit, et al.
Published: (2024)
by: Patel, Dhruvit, et al.
Published: (2024)
Reservoir Predictive Path Integral Control for Unknown Nonlinear Dynamics
by: Inoue, Daisuke, et al.
Published: (2025)
by: Inoue, Daisuke, et al.
Published: (2025)
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference
by: Niven, Robert K., et al.
Published: (2024)
by: Niven, Robert K., et al.
Published: (2024)
Adapting Physics-Informed Neural Networks for Bifurcation Detection in Ecological Migration Models
by: Yin, Lujie, et al.
Published: (2024)
by: Yin, Lujie, et al.
Published: (2024)
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions
by: Buzhardt, Jake, et al.
Published: (2024)
by: Buzhardt, Jake, et al.
Published: (2024)
Inferring stability properties of chaotic systems on autoencoders' latent spaces
by: Özalp, Elise, et al.
Published: (2024)
by: Özalp, Elise, et al.
Published: (2024)
Entropic Regression DMD (ERDMD) Discovers Informative Sparse and Nonuniformly Time Delayed Models
by: Curtis, Christopher W., et al.
Published: (2024)
by: Curtis, Christopher W., et al.
Published: (2024)
Adjoint Sensitivities of Chaotic Flows without Adjoint Solvers: A Data-Driven Approach
by: Ozan, Defne E., et al.
Published: (2024)
by: Ozan, Defne E., et al.
Published: (2024)
Similar Items
-
Dynamics-Informed Deep Learning for Predicting Extreme Events
by: Katsidoniotaki, Eirini, et al.
Published: (2026) -
Incorporating Coupling Knowledge into Echo State Networks for Learning Spatiotemporally Chaotic Dynamics
by: Chu, Kuei-Jan, et al.
Published: (2025) -
Prediction of Unobserved Bifurcation by Unsupervised Extraction of Slowly Time-Varying System Parameter Dynamics from Time Series Using Reservoir Computing
by: Tokuda, Keita, et al.
Published: (2024) -
Machine Learning for Predicting Chaotic Systems
by: Schötz, Christof, et al.
Published: (2024) -
Deep Learning for Prediction and Classifying the Dynamical behaviour of Piecewise Smooth Maps
by: S, Vismaya V, et al.
Published: (2024)