Beyond recurrent neural networks, other machine learning techniques can be explored for modeling the complex, nonlinear dynamics of tokamak plasmas. One approach is using physics-informed neural networks, where physical principles are explicitly encoded into the model architecture. This ensures that the learned dynamics adhere to known physical laws, improving the model's accuracy and interpretability. Additionally, Gaussian processes can be utilized for uncertainty quantification and modeling complex, non-linear relationships in the data. Reinforcement learning algorithms can also be employed for optimizing control strategies in tokamak operation, learning from interactions with the environment to achieve desired outcomes. By combining these techniques with recurrent neural networks, a comprehensive and robust modeling framework can be developed for tokamak plasma dynamics.