Lagrangian Neural Network

Lagrangian Neural Networks (LNNs) integrate principles of Lagrangian mechanics into neural network architectures to improve the learning and prediction of dynamical systems, particularly those governed by physical laws. Current research focuses on extending LNNs to handle dissipative systems, improving control performance through structured network designs, and developing variants like Lagrangian Graph Neural Networks (LGNNs) for modeling complex systems with varying topologies, such as articulated rigid bodies. This approach offers advantages in terms of accuracy, data efficiency, and generalizability compared to traditional methods, with applications ranging from fluid dynamics and precipitation nowcasting to robotics and control systems.