Copertina

Beyond Static Models: Hypernetworks for Adaptive and Generalizable Forecasting in Complex Parametric Dynamical Systems

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Vlachas, Pantelis R., Vlachas, Konstantinos, Chatzi, Eleni
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Chaotic Dynamics Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems
di: Heidenreich, Hunter S., et al.
Pubblicazione: (2024)

Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems
di: Chakraborty, Dibyajyoti, et al.
Pubblicazione: (2026)

Tailored Forecasting from Short Time Series via Meta-learning
di: Norton, Declan A., et al.
Pubblicazione: (2025)

Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge
di: Schmidt, Oliver T., et al.
Pubblicazione: (2026)

Invariant Measures in Time-Delay Coordinates for Unique Dynamical System Identification
di: Botvinick-Greenhouse, Jonah, et al.
Pubblicazione: (2024)

Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics
di: Liu, Wei, et al.
Pubblicazione: (2025)

Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing
di: Norton, Declan A., et al.
Pubblicazione: (2025)

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)

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

Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
di: Brenner, Manuel, et al.
Pubblicazione: (2022)

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

Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning
di: Zhang, Yuanzhao, et al.
Pubblicazione: (2025)

Learning finite symmetry groups of dynamical systems via equivariance detection
di: Calvo-Barlés, Pablo, et al.
Pubblicazione: (2025)

GEN2: A Generative Prediction-Correction Framework for Long-time Emulations of Spatially-Resolved Climate Extremes
di: Wang, Mengze, et al.
Pubblicazione: (2025)

Generative learning for nonlinear dynamics
di: Gilpin, William
Pubblicazione: (2023)

Zero-shot forecasting of chaotic systems
di: Zhang, Yuanzhao, et al.
Pubblicazione: (2024)

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network
di: Rostamijavanani, Abdolvahhab, et al.
Pubblicazione: (2024)

Sparse identification of quasipotentials via a combined data-driven method
di: Lin, Bo, et al.
Pubblicazione: (2024)

Prediction Beyond the Medium Range with an Atmosphere-Ocean Model that Combines Physics-based Modeling and Machine Learning
di: Patel, Dhruvit, et al.
Pubblicazione: (2024)

DyMixOp: A Neural Operator Designed from a Complex Dynamics Perspective with Local-Global Mixing for Solving PDEs
di: Lai, Pengyu, et al.
Pubblicazione: (2025)

Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference
di: Niven, Robert K., et al.
Pubblicazione: (2024)

Conditional Score-Based Modeling of Effective Langevin Dynamics
di: Giorgini, Ludovico T.
Pubblicazione: (2026)

Versatile Reservoir Computing for Heterogeneous Complex Networks
di: Du, Yao, et al.
Pubblicazione: (2025)

A Numerical Proof of Shell Model Turbulence Closure
di: Ortali, Giulio, et al.
Pubblicazione: (2022)

Hierarchy of extreme-event predictability in turbulence revealed by machine learning
di: Yang, Yuxuan, et al.
Pubblicazione: (2026)

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

CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting
di: Chen, Chuanqi, et al.
Pubblicazione: (2023)

Prediction of Unobserved Bifurcation by Unsupervised Extraction of Slowly Time-Varying System Parameter Dynamics from Time Series Using Reservoir Computing
di: Tokuda, Keita, et al.
Pubblicazione: (2024)

Conditioning on PDE Parameters to Generalise Deep Learning Emulation of Stochastic and Chaotic Dynamics
di: Shokar, Ira J. S., et al.
Pubblicazione: (2025)

Learning Quantum Data Distribution via Chaotic Quantum Diffusion Model
di: Tran, Quoc Hoan, et al.
Pubblicazione: (2026)

FEG-Pro: Forecast-Error Growth Profiling for Finite-Horizon Instability Analysis of Nonlinear Time Series
di: Velichko, Andrei, et al.
Pubblicazione: (2026)

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

Constants of Motion for Conserved and Non-conserved Dynamics
di: Zimmer, Michael F.
Pubblicazione: (2024)

Characterizing Nonlinear Dynamics via Smooth Prototype Equivalences
di: Friedman, Roy, et al.
Pubblicazione: (2025)

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

Complex fractal trainability boundary can arise from trivial non-convexity
di: Liu, Yizhou
Pubblicazione: (2024)

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

Backpropagation on Dynamical Networks
di: Tan, Eugene, et al.
Pubblicazione: (2022)

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

Deep Learning for Prediction and Classifying the Dynamical behaviour of Piecewise Smooth Maps
di: S, Vismaya V, et al.
Pubblicazione: (2024)