  | MAI, T. L. (2022). MACHINE LEARNING IN THE DESIGN SPACE EXPLORATION OF TSN NETWORKS [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/50862 |
 | MAI, T. L., & NAVET, N. (October 2021). Deep Learning to Predict the Feasibility of Priority-Based Ethernet Network Configurations. ACM Transactions on Cyber-Physical Systems, 5 (4), 1–26. doi:10.1145/3468890  Peer Reviewed verified by ORBi |
 | MAI, T. L., & NAVET, N. (2021). Improvements to Deep-Learning-based Feasibility Prediction of Switched Ethernet Network Configurations. In The 29th International Conference on Real-Time Networks and Systems (RTNS2021). Peer reviewed |
 | MAI, T. L., NAVET, N., & Migge, J. (2019). A Hybrid Machine Learning and Schedulability Method for the Verification of TSN Networks. In 15th IEEE International Workshop on Factory Communication Systems (WFCS2019). IEEE. doi:10.1109/WFCS.2019.8757948 Peer reviewed |
 | NAVET, N., MAI, T. L., & Migge, J. (2019). Using Machine Learning to Speed Up the Design Space Exploration of Ethernet TSN networks. University of Luxembourg. https://orbilu.uni.lu/handle/10993/38604 |
 | MAI, T. L., NAVET, N., & Migge, J. (2019). On the use of supervised machine learning for assessing schedulability: application to Ethernet TSN. In 27th International Conference on Real-Time Networks and Systems (RTNS 2019). doi:10.1145/3356401.3356409 Peer reviewed |