The proposed analog neural differential equation solver can be extended to various generative AI tasks beyond diffusion models by adapting the architecture and training methodology. One approach is to integrate the solver into variational autoencoders (VAEs) for tasks like image generation, where the latent space can be manipulated to generate diverse and realistic images. Additionally, the solver can be applied to sequential data generation tasks, such as text generation or music composition, by incorporating recurrent neural networks (RNNs) or transformers. The analog nature of the solver allows for efficient and continuous processing, making it suitable for real-time applications like video generation or interactive content creation. By optimizing the network structure and training algorithms, the solver can be tailored to specific generative tasks, offering a versatile and high-performance solution for a wide range of AI applications.