Prof. Steven Brunton, U. Washington
Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics
January 31, 2022
Abstract: This work describes how machine learning may be used to develop accurate and efficient nonlinear dynamical systems models for complex natural and engineered systems. Prof. Brunton will explore the sparse identification of nonlinear dynamics (SINDy) algorithm, which identifies a minimal dynamical system model that balances model complexity with accuracy, avoiding overfitting. This approach tends to promote models that are interpretable and generalizable, capturing the essential “physics” of the system. He will also discuss the importance of learning effective coordinate systems in which the dynamics may be expected to be sparse. This sparse modeling approach will be demonstrated on a range of challenging modeling problems in fluid dynamics, and Prof. Brunton will discuss how to incorporate these models into existing model-based control efforts. Because fluid dynamics is central to transportation, health, and defense systems, he will emphasize the importance of machine learning solutions that are interpretable, explainable, generalizable, and that respect known physics.
Dr. Steven L. Brunton is a Professor of Mechanical Engineering at the University of Washington. He is also an Adjunct Professor of Applied Mathematics and Computer Science, and a Data Science Fellow at the eScience Institute. Steve received B.S. in Mathematics from Caltech in 2006 and Ph.D. in Mechanical and Aerospace Engineering from Princeton in 2012. His research combines machine learning with dynamical systems to model and control systems in fluid dynamics, biolocomotion, optics, energy systems, and manufacturing. He is a co-author of three textbooks, received the University of Washington College of Engineering junior faculty and teaching awards, the Army and Air Force Young Investigator Program (YIP) awards, and the Presidential Early Career Award for Scientists and Engineers (PECASE).
Photo credits: https://www.me.washington.edu/facultyfinder/steve-brunton
Dr. Louis Pecora, NRL Washington
February 28, 2022
Dr. Lou Pecora is a research scientist at the Naval Research Laboratory, where he has been since 1977. There he heads a group in nonlinear dynamics of solid-state systems.
In 1990, together with Thomas Carroll, he demonstrated for the first time experimentally the control of chaotic systems via synchronization of chaos, a phenomenon that they discovered. In 2020, he and Thomas Carroll were among the Clarivate Citation Laureates for research in nonlinear dynamics including synchronization of chaotic systems. His 1990 work with Thomas Carroll on the subject has been cited over 13,000 times.
Photo credits: U.S. Naval Research Laboratory
Prof. Katharina Krischer, TU Munich
March 28, 2022
Prof. Katharina Krischer covers self-organization in physical and physicochemical systems as well as electrocatalysis. She focuses in particular on efficient photoelectrochemical and electrocatalytic energy conversion and universal mechanisms of structure formation processes in nonequilibrium systems.
Photo credits: https://www.ph.tum.de/about/diversity/gender/?language=en
Prof. M. Lakshmanan, Bharathidasan U.
April 25, 2022
Prof. Muthusamy Lakshmanan is a theoretical physicist a Ramanna fellow of the Department of Science and Technology at the Centre for Nonlinear Dynamics of Bharathidasan University.
Known for his research on nonlinear dynamics and for the development of Murali-Lakshmanan-Chua (MLC) Circuit, Lakshmanan is an elected fellow of all three major Indian science academies – Indian Academy of Sciences, Indian National Science Academy, and National Academy of Sciences, India – as well as of The World Academy of Sciences and the Royal Swedish Academy of Sciences.
Photo credits: https://en.wikipedia.org/wiki/Muthusamy_Lakshmanan
Dr. Viola Priesemann, MPIDS Göttingen
May 30, 2022
Dr. Viola Priesemann will speak on the topic – Spreading dynamics, self-organization, and stability in complex networks: From neural networks to COVID-19
Dr. Priesemann studied physics and neuroscience at the TU Darmstadt and did research at the École normal supérieure in Paris, at Caltech in California, and the Max Planck Institute for Brain Research in Frankfurt. After receiving Ph.D. in 2013, she became a Fellow at the Bernstein Center for Computational Neuroscience Göttingen in 2014. Today she heads a research group on the theory of neural systems at the Max Planck Institute for Dynamics and Self-Organization in Göttingen.