2 papers
eess.SY2025
On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach
Yuhan Liu, Máté Kiss, Roland Tóth +1
While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems where robustness to extrapolation/interpolation errors…
eess.SY2024
Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks
Yuhan Liu, Roland Tóth, Maarten Schoukens
Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical in…