On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Yuhan, Kiss, Máté, Tóth, Roland, Schoukens, Maarten |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Space-Filling Input Design for Nonlinear State-Space Identification
by: Kiss, Máté, et al.
Published: (2024)
by: Kiss, Máté, et al.
Published: (2024)
Least Costly Space-Filling Experiment Design for the Identification of a Nonlinear System
by: Kiss, Máté, et al.
Published: (2026)
by: Kiss, Máté, et al.
Published: (2026)
Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks
by: Liu, Yuhan, et al.
Published: (2024)
by: Liu, Yuhan, et al.
Published: (2024)
Koopman Form of Nonlinear Systems with Inputs
by: Iacob, Lucian Cristian, et al.
Published: (2022)
by: Iacob, Lucian Cristian, et al.
Published: (2022)
Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
by: Beintema, Gerben I., et al.
Published: (2023)
by: Beintema, Gerben I., et al.
Published: (2023)
Efficient Learning of Affine and Rational Dependency LPV Models With Linear Fractional Representation
by: Drenth, Roel, et al.
Published: (2026)
by: Drenth, Roel, et al.
Published: (2026)
Exact Finite Koopman Embedding of Block-Oriented Polynomial Systems
by: Iacob, Lucian Cristian, et al.
Published: (2025)
by: Iacob, Lucian Cristian, et al.
Published: (2025)
Learning-based model augmentation with LFRs
by: Hoekstra, Jan H., et al.
Published: (2024)
by: Hoekstra, Jan H., et al.
Published: (2024)
Learning-based augmentation of first-principle models: A linear fractional representation-based approach
by: Hoekstra, Jan H., et al.
Published: (2026)
by: Hoekstra, Jan H., et al.
Published: (2026)
Orthogonal-by-construction augmentation of physics-based input-output models
by: Györök, Bendegúz M., et al.
Published: (2025)
by: Györök, Bendegúz M., et al.
Published: (2025)
Baseline Results for Selected Nonlinear System Identification Benchmarks
by: Champneys, Max D., et al.
Published: (2024)
by: Champneys, Max D., et al.
Published: (2024)
Learning Koopman Models From Data Under General Noise Conditions
by: Iacob, Lucian Cristian, et al.
Published: (2025)
by: Iacob, Lucian Cristian, et al.
Published: (2025)
Data-driven augmentation of first-principles models under constraint-free well-posedness and stability guarantees
by: Györök, Bendegúz, et al.
Published: (2026)
by: Györök, Bendegúz, et al.
Published: (2026)
Online and Offline Space-Filling Input Design for Nonlinear System Identification: A Receding Horizon Control-Based Approach
by: Herkersdorf, Max, et al.
Published: (2025)
by: Herkersdorf, Max, et al.
Published: (2025)
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles
by: Floch, Kristóf, et al.
Published: (2025)
by: Floch, Kristóf, et al.
Published: (2025)
Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
by: van Otterdijk, G. J. E., et al.
Published: (2024)
by: van Otterdijk, G. J. E., et al.
Published: (2024)
Orthogonal projection-based regularization for efficient model augmentation
by: Györök, Bendegúz M., et al.
Published: (2025)
by: Györök, Bendegúz M., et al.
Published: (2025)
Identification of Port-Hamiltonian Differential-Algebraic Equations from Input-Output Data
by: Hagelaars, N., et al.
Published: (2026)
by: Hagelaars, N., et al.
Published: (2026)
A Direct State-Space Realization of Discrete-Time Linear Parameter-Varying Input-Output Models
by: Kon, Johan, et al.
Published: (2025)
by: Kon, Johan, et al.
Published: (2025)
State Derivative Normalization for Continuous-Time Deep Neural Networks
by: Weigand, Jonas, et al.
Published: (2024)
by: Weigand, Jonas, et al.
Published: (2024)
On-the-fly Surrogation for Complex Nonlinear Dynamics
by: Olucha, E. Javier, et al.
Published: (2025)
by: Olucha, E. Javier, et al.
Published: (2025)
Automated Linear Parameter-Varying Modeling of Nonlinear Systems: A Global Embedding Approach
by: Olucha, E. Javier, et al.
Published: (2025)
by: Olucha, E. Javier, et al.
Published: (2025)
Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems
by: Koelewijn, Patrick J. W., et al.
Published: (2023)
by: Koelewijn, Patrick J. W., et al.
Published: (2023)
Learning Stable and Robust Linear Parameter-Varying State-Space Models
by: Verhoek, Chris, et al.
Published: (2023)
by: Verhoek, Chris, et al.
Published: (2023)
Encoder initialisation methods in the model augmentation setting
by: Hoekstra, J. H., et al.
Published: (2026)
by: Hoekstra, J. H., et al.
Published: (2026)
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems
by: Wolff, Tobias M., et al.
Published: (2024)
by: Wolff, Tobias M., et al.
Published: (2024)
Computationally Efficient Sampling-Based Algorithm for Stability Analysis of Nonlinear Systems
by: Antal, Péter, et al.
Published: (2024)
by: Antal, Péter, et al.
Published: (2024)
Feedback Identification of conductance-based models
by: Burghi, Thiago B., et al.
Published: (2020)
by: Burghi, Thiago B., et al.
Published: (2020)
Space-Filling Regularization for Robust and Interpretable Nonlinear State Space Models
by: Klein, Hermann, et al.
Published: (2025)
by: Klein, Hermann, et al.
Published: (2025)
Nonlinear Bandwidth and Bode Diagrams based on Scaled Relative Graphs
by: Krebbekx, Julius P. J., et al.
Published: (2025)
by: Krebbekx, Julius P. J., et al.
Published: (2025)
Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
by: Ewering, Jan-Hendrik, et al.
Published: (2025)
by: Ewering, Jan-Hendrik, et al.
Published: (2025)
Measurements and System Identification for the Characterization of Smooth Muscle Cell Dynamics
by: Ozturk, Dilan, et al.
Published: (2024)
by: Ozturk, Dilan, et al.
Published: (2024)
Scaled Relative Graph Analysis of General Interconnections of SISO Nonlinear Systems
by: Krebbekx, Julius P. J., et al.
Published: (2025)
by: Krebbekx, Julius P. J., et al.
Published: (2025)
Learning Surrogate LPV State-Space Models with Uncertainty Quantification
by: Olucha, E. Javier, et al.
Published: (2026)
by: Olucha, E. Javier, et al.
Published: (2026)
Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees
by: de Jong, Thomas, et al.
Published: (2024)
by: de Jong, Thomas, et al.
Published: (2024)
Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification
by: Kon, Johan, et al.
Published: (2024)
by: Kon, Johan, et al.
Published: (2024)
A Taylor Series Approach to Correction of Input Errors in Gaussian Process Regression
by: Qureshi, Muzaffar, et al.
Published: (2025)
by: Qureshi, Muzaffar, et al.
Published: (2025)
System identification of biophysical neuronal models
by: Burghi, Thiago B., et al.
Published: (2020)
by: Burghi, Thiago B., et al.
Published: (2020)
Convex Equilibrium-Free Stability and Performance Analysis of Discrete-Time Nonlinear Systems
by: Koelewijn, Patrick J. W., et al.
Published: (2024)
by: Koelewijn, Patrick J. W., et al.
Published: (2024)
Safe Exploration for Nonlinear Processes Using Online Gaussian Process Learning
by: Tonini, Stefano, et al.
Published: (2026)
by: Tonini, Stefano, et al.
Published: (2026)
Similar Items
-
Space-Filling Input Design for Nonlinear State-Space Identification
by: Kiss, Máté, et al.
Published: (2024) -
Least Costly Space-Filling Experiment Design for the Identification of a Nonlinear System
by: Kiss, Máté, et al.
Published: (2026) -
Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks
by: Liu, Yuhan, et al.
Published: (2024) -
Koopman Form of Nonlinear Systems with Inputs
by: Iacob, Lucian Cristian, et al.
Published: (2022) -
Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
by: Beintema, Gerben I., et al.
Published: (2023)