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eess.SY2026
Online learning of neural state-space models
Bendegúz Györök, Tamás Péni, Maarten Schoukens +1
Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) models that achieve state-of-the-art perf…
eess.SY2026
Efficient stochastic model-predictive control based on the meta-state-space representation
Bendegúz Györök, Roland Tóth, Maarten Schoukens +1
Stochastic model-predictive control (SMPC) has evolved to a powerful framework for the control of stochastic dynamical systems. SMPC utilizes a probabilistic uncertainty descriptio…
eess.SY2026
Data-driven augmentation of first-principles models under constraint-free well-posedness and stability guarantees
Bendegúz Györök, Roel Drenth, Chris Verhoek +3
The integration of first-principles models with learning-based components, i.e., model augmentation, has gained increasing attention, as it offers higher model accuracy and faster…