Data-driven system identification using quadratic embeddings of nonlinear dynamics
Fuente:
arXiv
Saved in:
| Main Authors: | Klus, Stefan, N'konzi, Joel-Pascal Ntwali |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Data-driven network analysis using local delay embeddings
by: Klus, Stefan, et al.
Published: (2024)
by: Klus, Stefan, et al.
Published: (2024)
Learning dynamical systems from data: Gradient-based dictionary optimization
by: Tabish, Mohammad, et al.
Published: (2024)
by: Tabish, Mohammad, et al.
Published: (2024)
Dynamical systems and complex networks: A Koopman operator perspective
by: Klus, Stefan, et al.
Published: (2024)
by: Klus, Stefan, et al.
Published: (2024)
Optimization of randomized neural networks for transfer operator approximation
by: Tabish, Mohammad, et al.
Published: (2026)
by: Tabish, Mohammad, et al.
Published: (2026)
Data-driven approximation of transfer operators for mean-field stochastic differential equations
by: Ioannou, Eirini, et al.
Published: (2025)
by: Ioannou, Eirini, et al.
Published: (2025)
How deep is your network? Deep vs. shallow learning of transfer operators
by: Tabish, Mohammad, et al.
Published: (2025)
by: Tabish, Mohammad, et al.
Published: (2025)
Transfer operators on graphs: Spectral clustering and beyond
by: Klus, Stefan, et al.
Published: (2023)
by: Klus, Stefan, et al.
Published: (2023)
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)
Bayesian Transfer Operators in Reproducing Kernel Hilbert Spaces
by: Boshoff, Septimus, et al.
Published: (2025)
by: Boshoff, Septimus, et al.
Published: (2025)
BINDy -- Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte-Carlo
by: Champneys, Max D., et al.
Published: (2024)
by: Champneys, Max D., et al.
Published: (2024)
Clustering Time-Evolving Networks Using the Spatio-Temporal Graph Laplacian
by: Trower, Maia, et al.
Published: (2024)
by: Trower, Maia, et al.
Published: (2024)
Sparse identification of nonlinear dynamics with library optimization mechanism: Recursive long-term prediction perspective
by: Yonezawa, Ansei, et al.
Published: (2025)
by: Yonezawa, Ansei, 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)
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)
Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks
by: Gao, Mars Liyao, et al.
Published: (2025)
by: Gao, Mars Liyao, et al.
Published: (2025)
Data-driven identification of latent port-Hamiltonian systems
by: Rettberg, Johannes, et al.
Published: (2024)
by: Rettberg, Johannes, et al.
Published: (2024)
tLaSDI: Thermodynamics-informed latent space dynamics identification
by: Park, Jun Sur Richard, et al.
Published: (2024)
by: Park, Jun Sur Richard, et al.
Published: (2024)
Nonlocal Kramers-Moyal formulas and data-driven discovery of stochastic dynamical systems with multiplicative Lévy noise
by: Li, Yang, et al.
Published: (2026)
by: Li, Yang, et al.
Published: (2026)
Probabilistic function-on-function nonlinear autoregressive model for emulation and reliability analysis of dynamical systems
by: Song, Zhouzhou, et al.
Published: (2026)
by: Song, Zhouzhou, et al.
Published: (2026)
Sparse identification of quasipotentials via a combined data-driven method
by: Lin, Bo, et al.
Published: (2024)
by: Lin, Bo, et al.
Published: (2024)
Reservoir computing for system identification and predictive control with limited data
by: Williams, Jan P., et al.
Published: (2024)
by: Williams, Jan P., et al.
Published: (2024)
Identifying the nonlinear string dynamics with port-Hamiltonian neural networks
by: Linares, Maximino, et al.
Published: (2026)
by: Linares, Maximino, et al.
Published: (2026)
One-shot learning for the complex dynamical behaviors of weakly nonlinear forced oscillators
by: Ma, Teng, et al.
Published: (2026)
by: Ma, Teng, et al.
Published: (2026)
Recovering the state and dynamics of autonomous system with partial states solution using neural networks
by: Kag, Vijay
Published: (2024)
by: Kag, Vijay
Published: (2024)
Automatic feature identification in least-squares policy iteration using the Koopman operator framework
by: Zagabe, Christian Mugisho, et al.
Published: (2026)
by: Zagabe, Christian Mugisho, et al.
Published: (2026)
Machine Learning for the identification of phase-transitions in interacting agent-based systems: a Desai-Zwanzig example
by: Evangelou, Nikolaos, et al.
Published: (2023)
by: Evangelou, Nikolaos, et al.
Published: (2023)
Control of dynamical systems with neural networks
by: Böttcher, Lucas
Published: (2025)
by: Böttcher, Lucas
Published: (2025)
Data-driven approximation of Koopman operators and generators: Convergence rates and error bounds
by: Llamazares-Elias, Liam, et al.
Published: (2024)
by: Llamazares-Elias, Liam, et al.
Published: (2024)
Deep learning for model correction of dynamical systems with data scarcity
by: Tatsuoka, Caroline, et al.
Published: (2024)
by: Tatsuoka, Caroline, et al.
Published: (2024)
Stabilization of nonlinear systems with unknown delays via delay-adaptive neural operator approximate predictors
by: Bhan, Luke, et al.
Published: (2025)
by: Bhan, Luke, et al.
Published: (2025)
Explicit construction of recurrent neural networks effectively approximating discrete dynamical systems
by: Nakayama, Chikara, et al.
Published: (2024)
by: Nakayama, Chikara, et al.
Published: (2024)
Let's do the time-warp-attend: Learning topological invariants of dynamical systems
by: Moriel, Noa, et al.
Published: (2023)
by: Moriel, Noa, et al.
Published: (2023)
Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder
by: Gupta, Priyam, et al.
Published: (2023)
by: Gupta, Priyam, et al.
Published: (2023)
Parametric Taylor series based latent dynamics identification neural networks
by: Lin, Xinlei, et al.
Published: (2024)
by: Lin, Xinlei, et al.
Published: (2024)
Delay compensation of multi-input distinct delay nonlinear systems via neural operators
by: Bajraktari, Filip, et al.
Published: (2025)
by: Bajraktari, Filip, et al.
Published: (2025)
Efficient Approximation of Molecular Kinetics using Random Fourier Features
by: Nüske, Feliks, et al.
Published: (2023)
by: Nüske, Feliks, et al.
Published: (2023)
Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
by: Schulze, Jan C., et al.
Published: (2024)
by: Schulze, Jan C., et al.
Published: (2024)
Compression of the Koopman matrix for nonlinear physical models via hierarchical clustering
by: Nishikata, Tomoya, et al.
Published: (2024)
by: Nishikata, Tomoya, et al.
Published: (2024)
Gradient Flow Equations for Deep Linear Neural Networks: A Survey from a Network Perspective
by: Wendin, Joel, et al.
Published: (2025)
by: Wendin, Joel, et al.
Published: (2025)
Equation-informed data-driven identification of flow budgets and dynamics
by: Sevryugina, Nataliya, et al.
Published: (2024)
by: Sevryugina, Nataliya, et al.
Published: (2024)
Similar Items
-
Data-driven network analysis using local delay embeddings
by: Klus, Stefan, et al.
Published: (2024) -
Learning dynamical systems from data: Gradient-based dictionary optimization
by: Tabish, Mohammad, et al.
Published: (2024) -
Dynamical systems and complex networks: A Koopman operator perspective
by: Klus, Stefan, et al.
Published: (2024) -
Optimization of randomized neural networks for transfer operator approximation
by: Tabish, Mohammad, et al.
Published: (2026) -
Data-driven approximation of transfer operators for mean-field stochastic differential equations
by: Ioannou, Eirini, et al.
Published: (2025)