Fluid Structure Interaction

Fluid-structure interaction (FSI) studies the complex interplay between fluids and deformable structures, aiming to accurately model and predict their coupled behavior. Current research heavily utilizes machine learning, particularly neural networks (including deep operator networks, graph neural networks, and physics-informed neural networks), and differentiable simulation techniques to improve the efficiency and accuracy of FSI models, often focusing on reduced-order modeling for complex systems. These advancements are crucial for diverse applications, including optimizing the design of robotic swimmers, controlling morphing aircraft, and enhancing the understanding of phenomena like vortex-induced vibrations in engineering structures. The development of accurate and efficient FSI models is driving progress in fields ranging from robotics and aerospace to biomechanics and civil engineering.