👉 Fluid is an advanced neural network architecture designed to efficiently handle continuous input and output data, particularly excelling in tasks involving fluid dynamics simulations, time series prediction, and generative modeling. Unlike traditional models that process discrete data, Fluid utilizes a continuous-output approach, allowing it to directly output values without the need for intermediate discrete steps. This is achieved through a unique combination of convolutional and recurrent layers, enabling the model to capture temporal dependencies and spatial relationships in data seamlessly. By leveraging adaptive computation units (ACUs) and a novel attention mechanism, Fluid can dynamically allocate computational resources to different parts of the input, enhancing its performance and efficiency. This makes Fluid particularly suitable for applications requiring real-time processing and high-dimensional data handling, such as weather forecasting, fluid flow simulations, and complex generative tasks.
