Cover Image

Encoder initialisation methods in the model augmentation setting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hoekstra, J. H., Györök, B., Töth, R., Schoukens, M.
Format:	Preprint
Published:	2026
Subjects:	Systems and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning-based augmentation of first-principle models: A linear fractional representation-based approach
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)

Orthogonal projection-based regularization for efficient model augmentation
by: Györök, Bendegúz M., et al.
Published: (2025)

Learning-based model augmentation with LFRs
by: Hoekstra, Jan H., et al.
Published: (2024)

Data-driven augmentation of first-principles models under constraint-free well-posedness and stability guarantees
by: Györök, Bendegúz, et al.
Published: (2026)

Efficient Learning of Affine and Rational Dependency LPV Models With Linear Fractional Representation
by: Drenth, Roel, et al.
Published: (2026)

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)

Space-Filling Input Design for Nonlinear State-Space Identification
by: Kiss, Máté, et al.
Published: (2024)

Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
by: Beintema, Gerben I., et al.
Published: (2023)

Exact Finite Koopman Embedding of Block-Oriented Polynomial Systems
by: Iacob, Lucian Cristian, et al.
Published: (2025)

Koopman Form of Nonlinear Systems with Inputs
by: Iacob, Lucian Cristian, et al.
Published: (2022)

On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach
by: Liu, Yuhan, et al.
Published: (2025)

Identification of Port-Hamiltonian Differential-Algebraic Equations from Input-Output Data
by: Hagelaars, N., et al.
Published: (2026)

Feedback Identification of conductance-based models
by: Burghi, Thiago B., et al.
Published: (2020)

Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
by: van Otterdijk, G. J. E., et al.
Published: (2024)

System identification of biophysical neuronal models
by: Burghi, Thiago B., et al.
Published: (2020)

Baseline Results for Selected Nonlinear System Identification Benchmarks
by: Champneys, Max D., et al.
Published: (2024)

Improved Initialization for Port-Hamiltonian Neural Network Models
by: van Otterdijk, G. J. E., et al.
Published: (2026)

State Derivative Normalization for Continuous-Time Deep Neural Networks
by: Weigand, Jonas, et al.
Published: (2024)

A frequency-domain approach for estimating continuous-time diffusively coupled linear networks
by: Liang, Desen, et al.
Published: (2024)

Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees
by: de Jong, Thomas, et al.
Published: (2024)

Efficient identification of linear, parameter-varying, and nonlinear systems with noise models
by: Bemporad, Alberto, et al.
Published: (2025)

HiL Demonstration of Online Battery Capacity and Impedance Estimation with Minimal a Priori Parametrization Effort
by: Beckers, Camiel J. J., et al.
Published: (2024)

Measurements and System Identification for the Characterization of Smooth Muscle Cell Dynamics
by: Ozturk, Dilan, et al.
Published: (2024)

On the reduction of Linear Parameter-Varying State-Space models
by: Olucha, E. Javier, et al.
Published: (2024)

Physics-augmented models to simulate commercial adaptive cruise control (ACC) systems
by: He, Yinglong, et al.
Published: (2021)

Uncertainty Quantification on State-Based Conflict Detection and Resolution Algorithms
by: Rahman, Muhammad Fazlur, et al.
Published: (2025)

Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form
by: Verhoek, Chris, et al.
Published: (2024)

Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes
by: Li, Zhaoyang, et al.
Published: (2024)

Nonlinear Bandwidth and Bode Diagrams based on Scaled Relative Graphs
by: Krebbekx, Julius P. J., et al.
Published: (2025)

Reset Controller Analysis and Design for Unstable Linear Plants using Scaled Relative Graphs
by: Krebbekx, Julius P. J., et al.
Published: (2025)

Scaled Relative Graph Analysis of General Interconnections of SISO Nonlinear Systems
by: Krebbekx, Julius P. J., et al.
Published: (2025)

Scaled Relative Graph Analysis of Lur'e Systems and the Generalized Circle Criterion
by: Krebbekx, Julius P. J., et al.
Published: (2024)

Approximate non-linear model predictive control with safety-augmented neural networks
by: Hose, Henrik, et al.
Published: (2023)

Tensor-based reduction of linear parameter-varying state-space models
by: Terzin, Bogoljub, et al.
Published: (2025)

Fast data augmentation for battery degradation prediction
by: Li, Weihan, et al.
Published: (2025)

Convex Equilibrium-Free Stability and Performance Analysis of Discrete-Time Nonlinear Systems
by: Koelewijn, Patrick J. W., et al.
Published: (2024)

Direct Data-Driven State-Feedback Control of Linear Parameter-Varying Systems
by: Verhoek, Chris, et al.
Published: (2022)

Computationally Efficient Sampling-Based Algorithm for Stability Analysis of Nonlinear Systems
by: Antal, Péter, et al.
Published: (2024)