Deep learning for model correction of dynamical systems with data scarcity
Fuente:
arXiv
Guardado en:
| Autores principales: | Tatsuoka, Caroline, Xiu, Dongbin |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
DUE: A Deep Learning Framework and Library for Modeling Unknown Equations
por: Chen, Junfeng, et al.
Publicado: (2025)
por: Chen, Junfeng, et al.
Publicado: (2025)
A Training-Free Conditional Diffusion Model for Learning Stochastic Dynamical Systems
por: Liu, Yanfang, et al.
Publicado: (2024)
por: Liu, Yanfang, et al.
Publicado: (2024)
Deficiency of equation-finding approach to data-driven modeling of dynamical systems
por: Zhai, Zheng-Meng, et al.
Publicado: (2025)
por: Zhai, Zheng-Meng, et al.
Publicado: (2025)
Multi-fidelity Parameter Estimation Using Conditional Diffusion Models
por: Tatsuoka, Caroline, et al.
Publicado: (2025)
por: Tatsuoka, Caroline, et al.
Publicado: (2025)
Modeling Unknown Stochastic Dynamical System Subject to External Excitation
por: Chen, Yuan, et al.
Publicado: (2024)
por: Chen, Yuan, et al.
Publicado: (2024)
Machine-learning invariant foliations in forced systems for reduced order modelling
por: Szalai, Robert
Publicado: (2024)
por: Szalai, Robert
Publicado: (2024)
Learning dynamical systems from data: Gradient-based dictionary optimization
por: Tabish, Mohammad, et al.
Publicado: (2024)
por: Tabish, Mohammad, et al.
Publicado: (2024)
Deep learning and the rate of approximation by flows
por: Cheng, Jingpu, et al.
Publicado: (2026)
por: Cheng, Jingpu, et al.
Publicado: (2026)
Deep learning for predicting the occurrence of tipping points
por: Zhuge, Chengzuo, et al.
Publicado: (2024)
por: Zhuge, Chengzuo, et al.
Publicado: (2024)
Nonlocal Kramers-Moyal formulas and data-driven discovery of stochastic dynamical systems with multiplicative Lévy noise
por: Li, Yang, et al.
Publicado: (2026)
por: Li, Yang, et al.
Publicado: (2026)
Predictability of Observables of Dynamical Systems
por: Liu, Xinyu, et al.
Publicado: (2026)
por: Liu, Xinyu, et al.
Publicado: (2026)
A scalable generative model for dynamical system reconstruction from neuroimaging data
por: Volkmann, Eric, et al.
Publicado: (2024)
por: Volkmann, Eric, et al.
Publicado: (2024)
Improved deep learning of chaotic dynamical systems with multistep penalty losses
por: Chakraborty, Dibyajyoti, et al.
Publicado: (2024)
por: Chakraborty, Dibyajyoti, et al.
Publicado: (2024)
Control of dynamical systems with neural networks
por: Böttcher, Lucas
Publicado: (2025)
por: Böttcher, Lucas
Publicado: (2025)
When are dynamical systems learned from time series data statistically accurate?
por: Park, Jeongjin, et al.
Publicado: (2024)
por: Park, Jeongjin, et al.
Publicado: (2024)
One-shot learning for the complex dynamical behaviors of weakly nonlinear forced oscillators
por: Ma, Teng, et al.
Publicado: (2026)
por: Ma, Teng, et al.
Publicado: (2026)
A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data
por: Sorensen, Benedikt Barthel, et al.
Publicado: (2024)
por: Sorensen, Benedikt Barthel, et al.
Publicado: (2024)
Probabilistic function-on-function nonlinear autoregressive model for emulation and reliability analysis of dynamical systems
por: Song, Zhouzhou, et al.
Publicado: (2026)
por: Song, Zhouzhou, et al.
Publicado: (2026)
How deep is your network? Deep vs. shallow learning of transfer operators
por: Tabish, Mohammad, et al.
Publicado: (2025)
por: Tabish, Mohammad, et al.
Publicado: (2025)
Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithms
por: Adrian, Melissa, et al.
Publicado: (2026)
por: Adrian, Melissa, et al.
Publicado: (2026)
Reconstruction of dynamical systems from data without time labels
por: Zeng, Zhijun, et al.
Publicado: (2023)
por: Zeng, Zhijun, et al.
Publicado: (2023)
Data-driven system identification using quadratic embeddings of nonlinear dynamics
por: Klus, Stefan, et al.
Publicado: (2025)
por: Klus, Stefan, et al.
Publicado: (2025)
Machine learning identifies nullclines in oscillatory dynamical systems
por: Prokop, Bartosz, et al.
Publicado: (2025)
por: Prokop, Bartosz, et al.
Publicado: (2025)
Explicit construction of recurrent neural networks effectively approximating discrete dynamical systems
por: Nakayama, Chikara, et al.
Publicado: (2024)
por: Nakayama, Chikara, et al.
Publicado: (2024)
Let's do the time-warp-attend: Learning topological invariants of dynamical systems
por: Moriel, Noa, et al.
Publicado: (2023)
por: Moriel, Noa, et al.
Publicado: (2023)
Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data
por: Reiter, Sean, et al.
Publicado: (2026)
por: Reiter, Sean, et al.
Publicado: (2026)
Dealing with soft variables and data scarcity: lessons learnt from quantification in a participatory system dynamics modelling process
por: Irene Pluchinotta, et al.
Publicado: (2024)
por: Irene Pluchinotta, et al.
Publicado: (2024)
Recovering the state and dynamics of autonomous system with partial states solution using neural networks
por: Kag, Vijay
Publicado: (2024)
por: Kag, Vijay
Publicado: (2024)
Data-driven model order reduction for structures with piecewise linear nonlinearity using dynamic mode decomposition
por: Saito, Akira, et al.
Publicado: (2026)
por: Saito, Akira, et al.
Publicado: (2026)
Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection
por: Plotzki, Tim, et al.
Publicado: (2026)
por: Plotzki, Tim, et al.
Publicado: (2026)
Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time Series
por: Lee, Jonghyeon, et al.
Publicado: (2021)
por: Lee, Jonghyeon, et al.
Publicado: (2021)
Reservoir computing for system identification and predictive control with limited data
por: Williams, Jan P., et al.
Publicado: (2024)
por: Williams, Jan P., et al.
Publicado: (2024)
Dictionary learning for Kernel EDMD
por: Bolager, Erik Lien, et al.
Publicado: (2026)
por: Bolager, Erik Lien, et al.
Publicado: (2026)
Dynamics-Informed Deep Learning for Predicting Extreme Events
por: Katsidoniotaki, Eirini, et al.
Publicado: (2026)
por: Katsidoniotaki, Eirini, et al.
Publicado: (2026)
Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning
por: Alam, Mohammad Shah, et al.
Publicado: (2025)
por: Alam, Mohammad Shah, et al.
Publicado: (2025)
Decomposing heterogeneous dynamical systems with graph neural networks
por: Allier, Cédric, et al.
Publicado: (2024)
por: Allier, Cédric, et al.
Publicado: (2024)
Why and When Deep is Better than Shallow: Implementation-Agnostic State-Transition Model of Deep Learning
por: Sonoda, Sho, et al.
Publicado: (2025)
por: Sonoda, Sho, et al.
Publicado: (2025)
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)
por: Shokar, Ira J. S., et al.
Publicado: (2025)
How iteration order influences convergence and stability in deep learning
por: Dherin, Benoit, et al.
Publicado: (2025)
por: Dherin, Benoit, et al.
Publicado: (2025)
Learning Deep Dissipative Dynamics
por: Okamoto, Yuji, et al.
Publicado: (2024)
por: Okamoto, Yuji, et al.
Publicado: (2024)
Ejemplares similares
-
DUE: A Deep Learning Framework and Library for Modeling Unknown Equations
por: Chen, Junfeng, et al.
Publicado: (2025) -
A Training-Free Conditional Diffusion Model for Learning Stochastic Dynamical Systems
por: Liu, Yanfang, et al.
Publicado: (2024) -
Deficiency of equation-finding approach to data-driven modeling of dynamical systems
por: Zhai, Zheng-Meng, et al.
Publicado: (2025) -
Multi-fidelity Parameter Estimation Using Conditional Diffusion Models
por: Tatsuoka, Caroline, et al.
Publicado: (2025) -
Modeling Unknown Stochastic Dynamical System Subject to External Excitation
por: Chen, Yuan, et al.
Publicado: (2024)