Showing eess.SYShow all
3 papers · 1 filter
eess.SY2026
Learning Neural Hybrid Surrogates for Gradient-Based Falsification
Lasse Kötz, Knut Åkesson
Falsification of hybrid dynamical systems remains challenging due to mode-dependent dynamics and discrete transitions. In this work, we propose a surrogate-based falsification appr…
eess.SY2026
Optimal Control-Based Falsification of Learnt Dynamics via Neural ODEs and Symbolic Regression
Lasse Kötz, Jonas Sjöberg, Knut Åkesson
We present a falsification framework that integrates learned surrogate dynamics with optimal control to efficiently generate counterexamples for cyber-physical systems specified in…
eess.SY2019
Enhancing Temporal Logic Falsification with Specification Transformation and Valued Booleans
Johan Lidén Eddeland, Koen Claessen, Nicholas Smallbone +3
Cyber-Physical Systems (CPSs) are systems with both physical and software components, for example cars and industrial robots. Since these systems exhibit both discrete and continuo…