Differentiable Fluid

Differentiable fluid simulation leverages machine learning to create computationally efficient and differentiable models of fluid dynamics, enabling optimization and control of fluid systems. Current research focuses on developing novel neural network architectures, such as neural flow maps and spatially sparse neural fields, to improve accuracy and efficiency in simulating various fluid phenomena, including turbulence and complex fluid-object interactions. These advancements are impacting diverse fields by facilitating the design and control of complex fluidic systems, improving parameter identification in fluid mechanics, and accelerating the development of more accurate and efficient computational fluid dynamics simulations. The resulting improvements in simulation speed and accuracy have significant implications for engineering, robotics, and scientific discovery.