Phi-ML meets Engineering: Kalman-Bucy-Informed Neural Network for System Identification

Thursday 16 May 2024, 12:00 → 13:00 UTC

Identifying the ODE in a system of nonlinear, ordinary differential equations is vital for designing a robust controller. However, if the system is stochastic in its nature or if only noisy measurements are available, standard optimization algorithms for system identification usually fail. We present a new approach that combines the recent advances in physics-informed neural networks and the well-known achievements of Kalman filters in order to find parameters in a continuous-time system with noisy measurements. This way, available system knowledge is utilized and improved in order to obtain a more precise model.  We show that the method works for complex systems by identifying the parameters of a double pendulum.

More info: https://www.turing.ac.uk/events/phi-ml-meets-engineering