32 citations · 70 across the 23 of their papers we have counts for
6 papers · 1 filter
Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
Nikolaus Vertovec, Frederik Baymler Mathiesen, Thom Badings +2
Control barrier functions (CBFs) are a popular tool for safety certification of nonlinear dynamical control systems. Recently, CBFs represented as neural networks have shown great…
Data-Driven Abstraction and Synthesis for Stochastic Systems with Unknown Dynamics
Mahdi Nazeri, Thom Badings, Anne-Kathrin Schmuck +2
We study the automated abstraction-based synthesis of correct-by-construction control policies for stochastic dynamical systems with unknown dynamics. Our approach is to learn an a…
Probabilistic Alternating Simulations for Policy Synthesis in Uncertain Stochastic Dynamical Systems
Thom Badings, Alessandro Abate
A classical approach to formal policy synthesis in stochastic dynamical systems is to construct a finite-state abstraction, often represented as a Markov decision process (MDP). Th…
Best-Effort Policies for Robust Markov Decision Processes
Alessandro Abate, Thom Badings, Giuseppe De Giacomo +1
We study the common generalization of Markov decision processes (MDPs) with sets of transition probabilities, known as robust MDPs (RMDPs). A standard goal in RMDPs is to compute a…
Integrating Expert and Physics Knowledge for Modeling Heat Load in District Heating Systems
Francisco Souza, Thom Badings, Geert Postma +1
New residential neighborhoods are often supplied with heat via district heating systems (DHS). Improving the energy efficiency of a DHS is critical for increasing sustainability an…
Data-Driven Yet Formal Policy Synthesis for Stochastic Nonlinear Dynamical Systems
Mahdi Nazeri, Thom Badings, Sadegh Soudjani +1
The automated synthesis of control policies for stochastic dynamical systems presents significant challenges. A standard approach is to construct a finite-state abstraction of the…