Flow Control

Flow control research aims to optimize the movement of fluids or objects within fluids, primarily focusing on improving efficiency, safety, and precision. Current efforts leverage deep reinforcement learning, employing architectures like group-invariant networks and positional encoding to enhance learning speed and quality, alongside model-based approaches to reduce computational costs. These advancements are impacting diverse fields, from optimizing industrial processes like pesticide spraying and glass manufacturing to enabling safer and more efficient robotic systems for tasks such as high-rise building painting and marine exploration.