Assumed Density Filtering and Smoothing with Neural Network Surrogate Models

The study on Assumed Density Filtering and Smoothing with Neural Network Surrogate Models presents a significant advancement in state estimation for nonlinear systems. Traditional methods like the Kalman filter and Rauch-Tung-Striebel smoother excel in linear contexts, but the challenge lies in effectively propagating uncertainty in nonlinear scenarios. By employing a state-of-the-art analytic formula, the authors demonstrate that their method allows for accurate uncertainty propagation, particularly in stochastic Lorenz and Wiener systems. They argue for the use of cross entropy as a more suitable performance metric than RMSE, reinforcing the method's effectiveness. The findings indicate that this approach not only enhances state estimation accuracy but also facilitates optimal linear quadratic regulation when the state estimate is utilized for feedback, marking a notable step forward in the field of artificial intelligence and control systems.