Generative learning for nonlinear dynamics
Fuente:
arXiv
Saved in:
| Main Author: | Gilpin, William |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning
by: Zhang, Yuanzhao, et al.
Published: (2025)
by: Zhang, Yuanzhao, et al.
Published: (2025)
Zero-shot forecasting of chaotic systems
by: Zhang, Yuanzhao, et al.
Published: (2024)
by: Zhang, Yuanzhao, et al.
Published: (2024)
Transformers for dynamical systems learn transfer operators in-context
by: Bao, Anthony, et al.
Published: (2026)
by: Bao, Anthony, et al.
Published: (2026)
Panda: A pretrained forecast model for chaotic dynamics
by: Lai, Jeffrey, et al.
Published: (2025)
by: Lai, Jeffrey, et al.
Published: (2025)
Recurrences reveal shared causal drivers of complex time series
by: Gilpin, William
Published: (2023)
by: Gilpin, William
Published: (2023)
Tailored Forecasting from Short Time Series via Meta-learning
by: Norton, Declan A., et al.
Published: (2025)
by: Norton, Declan A., et al.
Published: (2025)
Learning finite symmetry groups of dynamical systems via equivariance detection
by: Calvo-Barlés, Pablo, et al.
Published: (2025)
by: Calvo-Barlés, Pablo, et al.
Published: (2025)
Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems
by: Heidenreich, Hunter S., et al.
Published: (2024)
by: Heidenreich, Hunter S., et al.
Published: (2024)
On the weight dynamics of learning networks
by: Sharafi, Nahal, et al.
Published: (2024)
by: Sharafi, Nahal, 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)
Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing
by: Norton, Declan A., et al.
Published: (2025)
by: Norton, Declan A., et al.
Published: (2025)
Hierarchy of extreme-event predictability in turbulence revealed by machine learning
by: Yang, Yuxuan, et al.
Published: (2026)
by: Yang, Yuxuan, et al.
Published: (2026)
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)
Beyond Static Models: Hypernetworks for Adaptive and Generalizable Forecasting in Complex Parametric Dynamical Systems
by: Vlachas, Pantelis R., et al.
Published: (2025)
by: Vlachas, Pantelis R., et al.
Published: (2025)
Data-driven model order reduction for structures with piecewise linear nonlinearity using dynamic mode decomposition
by: Saito, Akira, et al.
Published: (2026)
by: Saito, Akira, et al.
Published: (2026)
Classification of synchronization in nonlinear systems using ICO learning
by: Deka, J. P.
Published: (2024)
by: Deka, J. P.
Published: (2024)
Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble
by: Bocquet, Marc, et al.
Published: (2024)
by: Bocquet, Marc, et al.
Published: (2024)
Optimization hardness constrains ecological transients
by: Gilpin, William
Published: (2024)
by: Gilpin, William
Published: (2024)
Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data
by: Fung, Lloyd, et al.
Published: (2024)
by: Fung, Lloyd, et al.
Published: (2024)
Invariant Measures in Time-Delay Coordinates for Unique Dynamical System Identification
by: Botvinick-Greenhouse, Jonah, et al.
Published: (2024)
by: Botvinick-Greenhouse, Jonah, et al.
Published: (2024)
Sparse identification of quasipotentials via a combined data-driven method
by: Lin, Bo, et al.
Published: (2024)
by: Lin, Bo, et al.
Published: (2024)
Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge
by: Schmidt, Oliver T., et al.
Published: (2026)
by: Schmidt, Oliver T., et al.
Published: (2026)
Learning more physically realistic dynamics in machine-learning based weather forecasting with latent-space constraints
by: Fan, Hang, et al.
Published: (2025)
by: Fan, Hang, et al.
Published: (2025)
Universal replication of chaotic characteristics by classical and quantum machine learning
by: Bai, Sheng-Chen, et al.
Published: (2024)
by: Bai, Sheng-Chen, et al.
Published: (2024)
Optimal training of finitely-sampled quantum reservoir computers for forecasting of chaotic dynamics
by: Ahmed, Osama, et al.
Published: (2024)
by: Ahmed, Osama, et al.
Published: (2024)
Robust quantum reservoir computers for forecasting chaotic dynamics: generalized synchronization and stability
by: Ahmed, Osama, et al.
Published: (2025)
by: Ahmed, Osama, et al.
Published: (2025)
Tailored minimal reservoir computing: on the bidirectional connection between nonlinearities in the reservoir and in data
by: Prosperino, Davide, et al.
Published: (2025)
by: Prosperino, Davide, et al.
Published: (2025)
Unsupervised learning for anticipating critical transitions
by: Panahi, Shirin, et al.
Published: (2025)
by: Panahi, Shirin, et al.
Published: (2025)
DIRESA, a distance-preserving nonlinear dimension reduction technique based on regularized autoencoders
by: De Paepe, Geert, et al.
Published: (2024)
by: De Paepe, Geert, et al.
Published: (2024)
Globalizing the Carleman linear embedding method for nonlinear dynamics
by: Novikau, Ivan, et al.
Published: (2025)
by: Novikau, Ivan, et al.
Published: (2025)
Data-driven forced response analysis with min-max representations of nonlinear restoring forces
by: Saito, Akira, et al.
Published: (2026)
by: Saito, Akira, et al.
Published: (2026)
Discovering the dynamics of \emph{Sargassum} rafts' centers of mass
by: Beron-Vera, Francisco J., et al.
Published: (2025)
by: Beron-Vera, Francisco J., et al.
Published: (2025)
Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flow
by: De Jesús, Carlos E. Pérez, et al.
Published: (2023)
by: De Jesús, Carlos E. Pérez, et al.
Published: (2023)
Fourier neural operators for spatiotemporal dynamics in two-dimensional turbulence
by: Atif, Mohammad, et al.
Published: (2024)
by: Atif, Mohammad, et al.
Published: (2024)
Reservoir Computing Generalized
by: Kubota, Tomoyuki, et al.
Published: (2024)
by: Kubota, Tomoyuki, et al.
Published: (2024)
Deficiency of equation-finding approach to data-driven modeling of dynamical systems
by: Zhai, Zheng-Meng, et al.
Published: (2025)
by: Zhai, Zheng-Meng, et al.
Published: (2025)
Machine-learning optimized measurements of chaotic dynamical systems via the information bottleneck
by: Murphy, Kieran A., et al.
Published: (2023)
by: Murphy, Kieran A., et al.
Published: (2023)
Data-driven Mori-Zwanzig modeling of Lagrangian particle dynamics in turbulent flows
by: de Wit, Xander, et al.
Published: (2025)
by: de Wit, Xander, et al.
Published: (2025)
Multiple Descents in Deep Learning as a Sequence of Order-Chaos Transitions
by: Wei, Wenbo, et al.
Published: (2025)
by: Wei, Wenbo, et al.
Published: (2025)
A Numerical Proof of Shell Model Turbulence Closure
by: Ortali, Giulio, et al.
Published: (2022)
by: Ortali, Giulio, et al.
Published: (2022)
Similar Items
-
Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning
by: Zhang, Yuanzhao, et al.
Published: (2025) -
Zero-shot forecasting of chaotic systems
by: Zhang, Yuanzhao, et al.
Published: (2024) -
Transformers for dynamical systems learn transfer operators in-context
by: Bao, Anthony, et al.
Published: (2026) -
Panda: A pretrained forecast model for chaotic dynamics
by: Lai, Jeffrey, et al.
Published: (2025) -
Recurrences reveal shared causal drivers of complex time series
by: Gilpin, William
Published: (2023)