Imagen de Portada

Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Niven, Robert K., Cordier, Laurent, Mohammad-Djafari, Ali, Abel, Markus, Quade, Markus
Formato:	Preprint
Publicado:	2024
Materias:	Methodology Chaotic Dynamics Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data
por: Fung, Lloyd, et al.
Publicado: (2024)

Overcoming error-in-variable problem in data-driven model discovery by orthogonal distance regression
por: Fung, Lloyd
Publicado: (2025)

Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming
por: Qu, Yongquan, et al.
Publicado: (2024)

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

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

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

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

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

Filtering Dynamical Systems Using Observations of Statistics
por: Bach, Eviatar, et al.
Publicado: (2023)

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

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

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

Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
por: Butler, Kurt, et al.
Publicado: (2024)

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

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

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

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

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

Entropic Regression DMD (ERDMD) Discovers Informative Sparse and Nonuniformly Time Delayed Models
por: Curtis, Christopher W., et al.
Publicado: (2024)

Predicting Forced Responses of Probability Distributions via the Fluctuation-Dissipation Theorem and Generative Modeling
por: Giorgini, Ludovico T., et al.
Publicado: (2025)

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

Reduced-Order Modeling of Cyclo-Stationary Time Series Using Score-Based Generative Methods
por: Giorgini, Ludovico Theo, et al.
Publicado: (2025)

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

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

Backpropagation on Dynamical Networks
por: Tan, Eugene, et al.
Publicado: (2022)

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

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

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

Trans-Bifurcation Prediction of Dynamics in terms of Extreme Learning Machines with Control Inputs
por: Tadokoro, Satoru, et al.
Publicado: (2024)

Identifying Stochastic Dynamics from Non-Sequential Data (DyNoSeD)
por: Lu, Zhixin, et al.
Publicado: (2025)

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

Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application
por: Botvinick-Greenhouse, Jonah
Publicado: (2025)

Dynamics-Informed Deep Learning for Predicting Extreme Events
por: Katsidoniotaki, Eirini, et al.
Publicado: (2026)

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

Multivariate time series prediction using clustered echo state network
por: Hariharan, S., et al.
Publicado: (2025)

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

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

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

Controlling Dynamical Systems into Unseen Target States Using Machine Learning
por: Köglmayr, Daniel, et al.
Publicado: (2024)

Fourier neural operators for spatiotemporal dynamics in two-dimensional turbulence
por: Atif, Mohammad, et al.
Publicado: (2024)