PINN-parafoil: A physics-informed neural network method for complex parafoil dynamics simulating

Accurately solving complex parafoil dynamics is essential for simulating para-foil system behavior. However, traditional numerical integration methods struggle with computational efficiency due to the high complexity of these models. This paper introduces a physics-informed neural network approach (PINN-Parafoil) that efficiently estimates numerical solutions for complex parafoil dynamics. By leveraging the superior function approximation capabilities of neural networks, PINN-Parafoil delivers near closed-form solutions, overcoming the computational challenges associated with conventional integration techniques. Unlike standard neural network methods, PINN-Parafoil incorporates the governing physical laws of parafoil dynamics as prior constraints, ensuring that the model outputs align with both training data and underlying physical principles. To validate this approach, the PINN-Parafoil model was trained and tested against the traditional Runge–Kutta solver for the 9-degree-of-freedom (DOF) parafoil model. Experimental results show that PINN-Parafoil achieves 25 times greater computational efficiency compared to traditional methods, while maintaining high accuracy with negligible numerical differences from true values. The resulting motion curves exhibit consistent dynamic characteristics with reference trajectories. Additionally, ablation studies highlight the critical role of physical constraints in enhancing model accuracy and stability. PINN-Parafoil offers a fast, accurate, and reliable proxy for simulating complex parafoil dynamics. Its efficiency and effectiveness make it a promising tool for various applications, including parafoil system design, trajectory planning, and homing control. This method provides robust technical support for both research and practical implementations in these fields, setting a foundation for further exploration and refinement of physics-informed neural network methodologies.