Image de couverture de livre

Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Heidenreich, Hunter S., Vlachas, Pantelis R., Koumoutsakos, Petros
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Chaotic Dynamics Computational Physics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Beyond Static Models: Hypernetworks for Adaptive and Generalizable Forecasting in Complex Parametric Dynamical Systems
par: Vlachas, Pantelis R., et autres
Publié: (2025)

Zero-shot forecasting of chaotic systems
par: Zhang, Yuanzhao, et autres
Publié: (2024)

Transformers for dynamical systems learn transfer operators in-context
par: Bao, Anthony, et autres
Publié: (2026)

State Estimation Using Sparse DEIM and Recurrent Neural Networks
par: Farazmand, Mohammad
Publié: (2024)

Learning finite symmetry groups of dynamical systems via equivariance detection
par: Calvo-Barlés, Pablo, et autres
Publié: (2025)

Using Machine Learning and Neural Networks to Analyze and Predict Chaos in Multi-Pendulum and Chaotic Systems
par: Ramachandruni, Vasista, et autres
Publié: (2025)

Optimal training of finitely-sampled quantum reservoir computers for forecasting of chaotic dynamics
par: Ahmed, Osama, et autres
Publié: (2024)

Robust quantum reservoir computers for forecasting chaotic dynamics: generalized synchronization and stability
par: Ahmed, Osama, et autres
Publié: (2025)

Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction
par: Hemmer, Christoph Jürgen, et autres
Publié: (2024)

Generative learning for nonlinear dynamics
par: Gilpin, William
Publié: (2023)

Panda: A pretrained forecast model for chaotic dynamics
par: Lai, Jeffrey, et autres
Publié: (2025)

Can Transformers predict system collapse in dynamical systems?
par: Zhai, Zheng-Meng, et autres
Publié: (2026)

Learning more physically realistic dynamics in machine-learning based weather forecasting with latent-space constraints
par: Fan, Hang, et autres
Publié: (2025)

Recurrences reveal shared causal drivers of complex time series
par: Gilpin, William
Publié: (2023)

Mamba time series forecasting with uncertainty quantification
par: Pessoa, Pedro, et autres
Publié: (2025)

Temporal horizons in forecasting: a performance-learnability trade-off
par: Aceituno, Pau Vilimelis, et autres
Publié: (2025)

Network Dynamics-Based Framework for Understanding Deep Neural Networks
par: Lin, Yuchen, et autres
Publié: (2025)

Time-Lagged Recurrence: a data-driven method to estimate the predictability of dynamical systems
par: Dong, Chenyu, et autres
Publié: (2024)

Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning
par: Zhang, Yuanzhao, et autres
Publié: (2025)

GEN2: A Generative Prediction-Correction Framework for Long-time Emulations of Spatially-Resolved Climate Extremes
par: Wang, Mengze, et autres
Publié: (2025)

Tailored Forecasting from Short Time Series via Meta-learning
par: Norton, Declan A., et autres
Publié: (2025)

Invariant Measures in Time-Delay Coordinates for Unique Dynamical System Identification
par: Botvinick-Greenhouse, Jonah, et autres
Publié: (2024)

Sparse identification of quasipotentials via a combined data-driven method
par: Lin, Bo, et autres
Publié: (2024)

Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing
par: Norton, Declan A., et autres
Publié: (2025)

Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge
par: Schmidt, Oliver T., et autres
Publié: (2026)

Deficiency of equation-finding approach to data-driven modeling of dynamical systems
par: Zhai, Zheng-Meng, et autres
Publié: (2025)

Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study
par: Ghosh, Adrita, et autres
Publié: (2025)

Parameter Inference in Non-linear Dynamical Systems via Recurrence Plots and Convolutional Neural Networks
par: Lober, L., et autres
Publié: (2024)

PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systems
par: Farkane, Ayoub, et autres
Publié: (2025)

On the weight dynamics of learning networks
par: Sharafi, Nahal, et autres
Publié: (2024)

Shell Models on Recurrent Sequences: Fibonacci, Padovan and Other Series
par: Manfredini, Lorenzo, et autres
Publié: (2024)

pynamicalsys: A Python toolkit for the analysis of dynamical systems
par: Sales, Matheus Rolim, et autres
Publié: (2025)

Hierarchy of extreme-event predictability in turbulence revealed by machine learning
par: Yang, Yuxuan, et autres
Publié: (2026)

Discovering the dynamics of \emph{Sargassum} rafts' centers of mass
par: Beron-Vera, Francisco J., et autres
Publié: (2025)

Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
par: Bishnoi, Suresh, et autres
Publié: (2022)

Stability analysis of chaotic systems in latent spaces
par: Özalp, Elise, et autres
Publié: (2024)

Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble
par: Bocquet, Marc, et autres
Publié: (2024)

Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flow
par: De Jesús, Carlos E. Pérez, et autres
Publié: (2023)

Fourier neural operators for spatiotemporal dynamics in two-dimensional turbulence
par: Atif, Mohammad, et autres
Publié: (2024)

Inferring stability properties of chaotic systems on autoencoders' latent spaces
par: Özalp, Elise, et autres
Publié: (2024)