Kalman-Bucy Informed Neural Networks for System Identification
Offered By: Alan Turing Institute via YouTube
Course Description
Overview
Explore a cutting-edge approach to system identification in this hour-long lecture from the Alan Turing Institute. Delve into the challenges of identifying ordinary differential equations (ODEs) in nonlinear, stochastic systems with noisy measurements. Learn about a novel method that combines physics-informed neural networks with Kalman filter techniques to accurately determine parameters in continuous-time systems. Discover how this approach leverages existing system knowledge to create more precise models, even for complex systems like double pendulums. Gain insights into the importance of robust system identification for controller design and see how this innovative technique overcomes the limitations of standard optimization algorithms.
Syllabus
Tobias Heinrich Nagel - Kalman Bucy informed Neural Networks for System Identification
Taught by
Alan Turing Institute
Related Courses
Neural Networks for Machine LearningUniversity of Toronto via Coursera Good Brain, Bad Brain: Basics
University of Birmingham via FutureLearn Statistical Learning with R
Stanford University via edX Machine Learning 1—Supervised Learning
Brown University via Udacity Fundamentals of Neuroscience, Part 2: Neurons and Networks
Harvard University via edX