SMRI Seminar: Chandramoorthy -- Learning of, for, and by dynamical systems

Title: Learning of, for, and by dynamical systems 

Speaker: Nisha Chandramoorthy 

Abstract: Across scientific and engineering applications, we are interested in sampling
typical or observable states achieved by complex dynamical systems.  To generate such
samples, we are often given approximate numerical models and data at various fidelities
and time/length scales.  Our goals are to develop rigorous computations that can scale
to high-dimensional settings like atmospheric and oceanic dynamics and aerospace systems
to answer questions such as: i) how can we reduce the dimension of Bayesian sampling
algorithms even in complex nonlinear systems? ii) how do small perturbations in the
dynamical model/equations affect its long-term behavior? iii) how can we learn effective
low-dimensional representations of the dynamics so as to predict desired quantities? We
will look at low-dimensional examples of chaotic systems that illustrate why answering
these questions is challenging and explore principled approaches to circumvent these
challenges.  

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