Mechanical and Civil Engineering Seminar
Tuesday May 23, 2017 11:00 AM
"High‐fidelity probabilistic model identification and monitoring of nonlinear dynamical systems"
Speaker: Audrey Olivier
Location: Gates-Thomas 135
In Recent years we have seen a growing interest in the development of on‐line system identification methods, which make use of measurements from a system to learn the equations and parameters characterizing it, possibly in real‐time. These methods find a wide variety of applications, including but not restricted to, damage detection and structural health monitoring in civil engineering, control and diagnostics of mechanical systems, state estimation of chemical processes, improved modelling of biomechanical systems. In particular, Bayesian inference methods are very attractive due to their ability to take into account uncertainties in the system and measurements, as well as stochastic input excitations, and yield results in a probabilistic format thus enabling more accurate performance assessment of the systems of interest. The Bayesian framework is also well‐suited to address ill‐conditioned problems, where not all parameters can be learnt from the available noisy data, a problem which will surely arise when considering large dimensional systems. A major challenge regarding on‐line Bayesian filtering algorithms lies in achieving good accuracy for large dimensional systems and complex nonlinear non‐Gaussian systems, where non‐Gaussianity can arise for instance in systems which are not globally identifiable. In using algorithmic enhancements of filtering techniques, mainly based on innovative ways to reduce the dimensionality of the problem at hand, one can obtain a good trade‐off between accuracy and computational complexity of the learning algorithms. For instance, for particle filtering techniques (sampling‐based algorithms) subjected to the so‐called curse of dimensionality, the concept of Rao‐Blackwellisation can be used to greatly reduce the dimension of the sampling space. On the other hand, one can also build upon nonlinear Kalman filtering techniques, which are very computationally efficient, and expand their capabilities to non‐Gaussian distributions. Thus, using some prior knowledge of the system, one can derive efficient Bayesian inference techniques, potentially enabling real‐time monitoring, or improved modelling capabilities, for a wide variety of nonlinear dynamical systems. *This lecture is part of the Young Investigators Lecture Series sponsored by the Caltech Division of Engineering & Applied Science.
Series Mechanical and Civil Engineering Seminar
Contact: Sonya Lincoln at 626-395-3385 email@example.com