Rich and Expressive Specification of Continuous-Learning Cyber-Physical Systems. - Thomas Flinkow; Barak A. Pearlmutter; Rosemary Monahan (DSN-S, 2023)

Comparing Differentiable Logics for Learning Systems: A Research Preview. - Thomas Flinkow; Barak A. Pearlmutter; Rosemary Monahan (FMAS@iFM, 2023)

Towards Correct-by-Construction Machine-Learnt Models. - Thomas Flinkow; Barak A. Pearlmutter; Rosemary Monahan (PhD@IFM, 2024)

Cyclone: A New Tool for Verifying/Testing Graph-Based Structures - Tool Paper. - Hao Wu 017; Thomas Flinkow; Dominique Méry (TAP, 2024)

Creating a Formally Verified Neural Network for Autonomous Navigation: An Experience Report. - Syed Ali Asadullah Bukhari; Thomas Flinkow; Medet Inkarbekov; Barak A. Pearlmutter; Rosemary Monahan (FMAS@iFM, 2024)

Comparing differentiable logics for learning with logical constraints. - Thomas Flinkow; Barak A. Pearlmutter; Rosemary Monahan (Sci. Comput. Program., 2025)

A Generalised Framework for Property-Driven Machine Learning. - Thomas Flinkow; Marco Casadio; Colin Kessler; Rosemary Monahan; Ekaterina Komendantskaya (CoRR, 2025)

Neural Network Verification for Gliding Drone Control: A Case Study. - Colin Kessler; Ekaterina Komendantskaya; Marco Casadio; Ignazio Maria Viola; Thomas Flinkow; Albaraa Ammar Othman; Alistair Malhotra; Robbie McPherson (CoRR, 2025)