Degradation of overhead line cables is an important issue when considering the renewal of a large network, such as that managed by Rte. The problem of cable vibrations that lead to breaking of strands through fretting fatigue is a well-studied and prominent cause of damage. Experiments and simulations at reduced scale and under controlled conditions (Brika & Laneville, 1996, Zhang et al., 2021) have allowed considerable insight into the mechanisms to be gained and for industry standard models to be put in place (Said et al., 2024). However, the problem is that the laboratory and simulated conditions upon which the models are based do not represent the variability of atmospheric conditions over long periods. The wind, which causes the vibrations, is inherently turbulent and varies strongly over short time periods and small distances, where real cables often measure several hundred meters in length. An approach that is both quick and physically realistic would help substantially, and physically informed machine learning presents a valuable opportunity in this case. How the PINNS approach can be usefully applied to this problem is the subject of this work, which was started in Redford et al. (2024). However, a rigorous application of PINNS could resolve certain problems that were encountered. Long Short-Term Memory (LSTM, Fang and Wang, 2024) is thought to be particularly important. To progress, an evaluation needs to be made of the best neural network approach for this application. It will be interesting to know to what extent three-dimensional fluid-flow effects can be included. Further testing for spatial and time varying wind speed conditions will allow us to have confidence in the predictions under simulated realistic conditions. This is a substantial work program. At the time of the conference, we expect to have results that demonstrate the feasibility of PINNS applied to cable vibrations. REFERENCES Brika, D., & Laneville, A. (1996). IEEE Transactions on power delivery, 11(2), 1145-1152. Fan, X., & Wang, J. X. (2024). J. Comp. Phys., 496, 112584. Redford, J.A., Gueguin, M., Hafid, F., Ancellin, M., Ghidaglia, JM. (2024). In: Gattulli, et al. (eds) Dynamics and Aerodynamics of Cables. ISDAC 2023. Lect. Notes Civil Eng., vol 399. Springer. Said, J., Cieren, E., Redford, J., Gueguin, M., Capillon, R., Ancellin, M. (2024), CIGRE, 2024. Zhang, L., Redford, J., Hafid, F., Ghidaglia, J. M., & Gueguin, M. (2021). Eur. J. Mech. B Fluids, 85,