Professor Nathan Kutz will hold series of four lectures on Tuesdays from 14 November until 5 December 2023.

Lecture Room 402 in the CDT space:

Enter Sherfield building from the walkway, on the other side of the staircase on the righthand side you see the door which you need to enter, take a stairs or a lift to the 4th floor and you see the door to the CDT space. You look for the room there.

Abstract:   Data-driven models are critically enabling in many application areas where the underlying dynamics are unknown or only partially known, or where high-fidelity simulations are computationally expensive to generate.  The ability to produce accurate, low-rank, proxy models enable dynamic models to transform the representation and characterization of such systems.  Data-driven algorithms have emerged as a viable and critically enabling methodology that is typically empowered by machine learning algorithms.  Indeed, there are a diversity of mathematical algorithms that can be used to produce data-driven models including (i) dynamic mode decomposition, (ii) sparse identification for nonlinear dynamics, and (iii) neural networks.   Each of these methods are highlighted here with a view towards producing proxy, or reduced order, models that enable efficient computations of high-dimensional systems.  Moreover, these methods can be used with direct measurement data, computational data, or both in generating stable representations of the dynamics.  The course will focus on theory and computation with implementation of algorithms in python using a combination of packages (pyDMD, pySINDy) and deep learning algorithms (PyTorch)

Bio:  Nathan Kutz is the Yasuko Endo and Robert Bolles Professor of Applied Mathematics and Electrical and Computer Engineering at the University of Washington, having served as chair of applied mathematics from 2007-2015. He is also the Director of the AI Institute in Dynamic Systems (dynamicsAI.org).  He received the BS degree in physics and mathematics from the University of Washington in 1990 and the Phd in applied mathematics from Northwestern University in 1994. He was a postdoc in the applied and computational mathematics program at Princeton University before taking his faculty position. He has a wide range of interests, including neuroscience to fluid dynamics where he integrates machine learning with dynamical systems and control. During the academic year 2023-2024, he will be a visiting professor at Imperial College London and the Alan Turning Institute.