Copertina

ChaosNetBench: Benchmarking Spatio-Temporal Graph Neural Networks on Chaotic Lattice Dynamics

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Moges, Henok Tenaw, Skokos, Charalampos, Moodley, Deshendran
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Artificial Intelligence Chaotic Dynamics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A lightweight Spatial-Temporal Graph Neural Network for Long-term Time Series Forecasting
di: Moges, Henok Tenaw, et al.
Pubblicazione: (2025)

On the behavior of the Generalized Alignment Index (GALI) method for dissipative systems
di: Moges, Henok Tenaw, et al.
Pubblicazione: (2025)

Hamiltonian Chaos: From Galactic Dynamics to Plasma Physics
di: Moges, Henok Tenaw
Pubblicazione: (2025)

The evolution of the phase space structure along pitchfork and period-doubling bifurcations in a 3D galactic bar potential
di: Moges, Henok Tenaw, et al.
Pubblicazione: (2024)

Kinetic versus Magnetic Chaos in Toroidal Plasmas: A systematic quantitative comparison
di: Moges, Henok T., et al.
Pubblicazione: (2023)

Manifold dynamics and orbital properties in a two-dimensional galactic model
di: Theron, Dylan, et al.
Pubblicazione: (2025)

Efficient detection of chaos through the computation of the Generalized Alignment Index (GALI) by the multi-particle method
di: Manda, Bertin Many, et al.
Pubblicazione: (2024)

Horizon-Constrained Rashomon Sets for Chaotic Forecasting
di: Kale, Gauri, et al.
Pubblicazione: (2026)

Using Lagrangian descriptors to reveal the phase space structure of dynamical systems described by fractional differential equations: Application to the Duffing oscillator
di: Theron, Dylan, et al.
Pubblicazione: (2025)

Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
di: Hu, Jiaxi, et al.
Pubblicazione: (2024)

Regularity and reentry basins of low Earth orbits in the $J_{2}$-solar radiation pressure problem
di: Barbis, Cassandra, et al.
Pubblicazione: (2026)

Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction
di: Hemmer, Christoph Jürgen, et al.
Pubblicazione: (2024)

Using Machine Learning and Neural Networks to Analyze and Predict Chaos in Multi-Pendulum and Chaotic Systems
di: Ramachandruni, Vasista, et al.
Pubblicazione: (2025)

On the efficient numerical computation of covariant Lyapunov vectors
di: Plessis, Jean-Jacq du, et al.
Pubblicazione: (2025)

A Novel Approach for Estimating Largest Lyapunov Exponents in One-Dimensional Chaotic Time Series Using Machine Learning
di: Velichko, A., et al.
Pubblicazione: (2025)

When is a System Discoverable from Data? Discovery Requires Chaos
di: Shumaylov, Zakhar, et al.
Pubblicazione: (2025)

Multiple Descents in Deep Learning as a Sequence of Order-Chaos Transitions
di: Wei, Wenbo, et al.
Pubblicazione: (2025)

From Complex Dynamics to DynFormer: Rethinking Transformers for PDEs
di: Lai, Pengyu, et al.
Pubblicazione: (2026)

Out-of-Domain Generalization in Dynamical Systems Reconstruction
di: Göring, Niclas, et al.
Pubblicazione: (2024)

Using Echo-State Networks to Reproduce Rare Events in Chaotic Systems
di: Erofeev, Anton, et al.
Pubblicazione: (2025)

Incorporating Coupling Knowledge into Echo State Networks for Learning Spatiotemporally Chaotic Dynamics
di: Chu, Kuei-Jan, et al.
Pubblicazione: (2025)

True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics
di: Hemmer, Christoph Jürgen, et al.
Pubblicazione: (2025)

Minimal Deterministic Echo State Networks Outperform Random Reservoirs in Learning Chaotic Dynamics
di: Martinuzzi, Francesco
Pubblicazione: (2025)

Artificial Intelligence for EEG Prediction: Applied Chaos Theory
di: Jorgsson, Vincent
Pubblicazione: (2023)

A Numerical Study of Chaotic Dynamics of K-S Equation with FNOs
di: Khetrapal, Surbhi, et al.
Pubblicazione: (2024)

Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems
di: Shokar, Ira J. S., et al.
Pubblicazione: (2025)

Machine Learning for Predicting Chaotic Systems
di: Schötz, Christof, et al.
Pubblicazione: (2024)

Chaotic CNN for Limited Data Image Classification
di: M, Anusree, et al.
Pubblicazione: (2026)

Network Dynamics-Based Framework for Understanding Deep Neural Networks
di: Lin, Yuchen, et al.
Pubblicazione: (2025)

Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians
di: Engelken, Rainer
Pubblicazione: (2023)

OceanNet: A principled neural operator-based digital twin for regional oceans
di: Chattopadhyay, Ashesh, et al.
Pubblicazione: (2023)

Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
di: Bishnoi, Suresh, et al.
Pubblicazione: (2022)

Chaotic Switching In The Minimal Pendula Network
di: Ebrahimzadeh, Pezhman, et al.
Pubblicazione: (2026)

Integrating Score-Based Generative Modeling and Neural ODEs for Accurate Representation of Multiscale Chaotic Dynamics
di: Del Felice, Giulio, et al.
Pubblicazione: (2025)

Chaotic Dynamics and Fractal Geometry in Ring Lattice Systems of Nonchaotic Rulkov Neurons
di: Le, Brandon B.
Pubblicazione: (2024)

Temperature Dependent Mechanical and Structural Properties of Uniaxially Strained Planar Graphene
di: Erasmus, Sané, et al.
Pubblicazione: (2025)

Machine-Precision Prediction of Low-Dimensional Chaotic Systems
di: Schötz, Christof, et al.
Pubblicazione: (2025)

Adjoint Sensitivities of Chaotic Flows without Adjoint Solvers: A Data-Driven Approach
di: Ozan, Defne E., et al.
Pubblicazione: (2024)

PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systems
di: Farkane, Ayoub, et al.
Pubblicazione: (2025)

LanTu: Dynamics-Enhanced Deep Learning for Eddy-Resolving Ocean Forecasting
di: Zheng, Qingyu, et al.
Pubblicazione: (2025)