Latest Research in Chaos control and synchronization
18 research papers · 2026 median publication year
Top Research Topics in Chaos control and synchronization
- Machine Learning — 5 papers
- Chaos control and synchronization — 2 papers
- Statistical Mechanics — 1 papers
- Machine Learning and Algorithms — 1 papers
- Markov Chains and Monte Carlo Methods — 1 papers
- Neurons and Cognition — 1 papers
- Social and Information Networks — 1 papers
- Methodology — 1 papers
- Nonlinear Dynamics and Pattern Formation — 1 papers
- Machine Learning — 1 papers
Highest-Cited Papers
- Entropy Estimates from Stochastic Interpolants
- Conservation Buys Stability and Factoring Buys Counterfactuals in Physical World Models
- Learning stochasticity via a nonparametric approach to state-dependent noise estimation
- Principled Koopman Representations with Kalman Inference for Efficient Time-Series Prediction
- Neural Surrogate HMC: On Using Neural Likelihoods for Hamiltonian Monte Carlo in Simulation‐Based Inference
- Stationary covariance spectra of discrete-time non-normal random recurrent dynamics
- Observability conditions for neural state-space models with eigenvalues and their roots of unity
- Collective Hysteresis and Multistability in Threshold Networks
- Dynamic models with $p$ parameters are identified by $2p+1$ random features
- Linearized subspace refinement framework to expose hidden accuracy in trained neural networks
- Discrete-Time Chaotic Maps Featuring Hidden Chaotic Attractors
- An alternate mechanism for the emergence of localized oscillatory patterns in networked reaction–diffusion systems
- Autoencoders in Function Space
- Extreme Events in a Hidden Hyperchaotic System Without Equilibria
- Early warning signals for synchronization transitions from partial observations
- Deep-Control BSDE: Layerwise Brownian-Weighted Regression for High-Dimensional Semilinear PDEs
- A Two-Channel F-Transform Representation for Early Trajectory Characterization in Iterated Correlation Dynamics
- StocBench: A Benchmark for Generative Modeling of Stochastic Dynamics