Optimisation of physics-informed neural network architecture and training for tokamak equilibrium reconstruction

Reconstructing the plasma state is a central challenge in nuclear fusion experiments, as it is essential for understanding and predicting plasma behaviour. Physics-informed neural networks (PINNs), especially when combined with a multi-diagnostic approach, offer powerful advantages for addressing this problem. PINNs embed the governing physical laws directly into the learning process through differential equation constraints, enabling them to integrate sparse or noisy measurements while maintaining physical consistency. This makes them particularly suitable for equilibrium reconstruction, where they can incorporate diagnostic data as boundary conditions and naturally enforce the structure of the magnetohydrodynamic equations. Moreover, the use of multiple diagnostics helps over-constrain the system, reducing uncertainties and mitigating the ill-posedness characteristic of the plasma core region. Starting from results obtained in previous works [1] where the capabilities of multi-diagnostics equilibrium reconstruction through PINNs were demonstrated, in this work we perform several parametric studies to optimise both the neural network architecture and the training procedure. We examine the impact of automatically adjusting the relative weights between data and physics losses during training, the role of specialised physics-based network’s layers informed by prior knowledge or plasma state hypotheses, the choice of hidden layers’ activation function, and the benefits of initialising training from a pre-trained network. These analyses provide guidelines for designing the most effective neural network and training strategy for specific plasma conditions.