Miller Family Endowed Mathematics Lecture Series
Author: vsiefken
Join us for an engaging lecture by our guest speaker, J. Nathan Kutz, Boeing Professor of AI and Data-Driven Engineering at the University of Washington. Professor Kutz will explore cutting-edge strategies for using machine learning and AI to uncover complex scientific and engineering models from data. Learn how neural networks and data-driven methods are revolutionizing our understanding of nonlinear dynamical systems and accelerating discovery across disciplines.
Wed, Sept 10 – “Shallow Recurrent Decoders for the Automated Discovery of Physical Models”
Math Colloquium in 202 Carver Hall (1:00 pm).
Thurs, Sept 11 – “Accelerating Scientific Discovery with Machine Learning and AI”
Public Lecture in 3580 Memorial Union (6:00pm) Reception to follow.
Fri, Sept 12 – “Data-Driven Model Order Reduction for PDEs”
Computational and Applied Math Seminar in 401 Carver Hall (1:00pm).
Thursday, Sep 11, 2025
6:00 pm
3580 Memorial Union
Speaker: J. Nathan Kutz
A major challenge in the study of science and engineering systems is that of model discovery: turning data into dynamical models that are not just predictive but provide insight into the nature of the underlying physics and dynamics that generated the data. This lecture will introduce data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data. Neural networks are used in targeted ways to aid in the model reduction process. These approaches provide a suite of mathematical strategies for reducing the data required to discover and model unknown phenomena, giving a robust paradigm for modern AI-aided learning of physics and engineering principles.
J. Nathan Kutz is the Boeing Professor of AI and Data-Driven Engineering in the Department of Applied Mathematics and Electrical and Computer Engineering at the University of Washington. Professor Kutz is also the director of the university’s AI Institute in Dynamic Systems. His bachelor’s degree in physics and mathematics is from the University of Washington and his doctorate in applied mathematics is from Northwestern University. Professor Kutz’s interests include neuroscience and fluid dynamics, where he integrates machine learning with dynamical systems and control.