Synchronising coronary artery system via physics-informed neural feedback control

Synchronisation of nonlinear biomedical systems remains a critical challenge in cardiovascular modelling. Traditional control strategies often lack robustness when applied to chaotic and parameter-sensitive coronary artery models. The present work introduces a physics-informed neural network based feedback control framework. It directly embeds the governing differential equations into the learning process. This approach allows accurate modelling and control of highly nonlinear dynamics. The developed method ensures rapid and stable synchronisation of diseased and healthy coronary artery system states. It achieves a reduction in mean absolute error by over 80% and maintains a root mean square error below 0.05. These results are obtained in comparison with baseline linear state-feedback controllers. High Pearson correlation coefficients ( ≥ 0.90) confirm accurate trajectory tracking under diverse initial conditions. Overall, the findings emphasise the advantages of integrating physics-informed learning with control design. This integration offers a scalable approach to biomedical system synchronisation. Furthermore, it lays a strong foundation for future studies on uncertainty handling and efficient control implementation.