Water Treading Dynamical Study Based on Neural Network and Genetic Algorithm

Water treading, as a multidisciplinary practical skill, encompasses domains such as physics, physiology, and psychology. Its physical principles include the equilibrium of buoyancy and gravitational forces, the dynamic coupling of action and reaction forces in fluid dynamics, and the optimization of energy expenditure. This study proposes a computer simulation-based research methodology, neural network algorithm, genetic algorithm, integrating fluid dynamics software such as RealFlow and STAR-CCM+, to simulate and study multiphysics coupling behaviors during human water treading. It further explores the application potential of such simulations in fields including athletic training, cinematic visual effects, and emergency rescue scenarios. The research systematically analyzes technical challenges, such as computational complexity and biomechanical modeling limitations, while outlining future directions like AI-driven parameter optimization and real-time interactive simulation. This work provides theoretical support and practical references for interdisciplinary technology integration, aiming to advance innovative applications in scientific research and engineering practices.