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Welcome to our research group's website!

Our group specializes in data-driven modeling of dynamical systems for mechanical engineering applications. In particular, our research lies at the exciting and fertile intersection between data-science methods, dynamics and control theory, and fluid mechanics applications.

News

  • Benjamin Reyes joined the group as an undergraduate research assistant—welcome! Aug 24, 2022

  • B. Herrmann gave a talk on Dimensionality reduction for dynamical systems at CMM Pucón 2022. Aug 18, 2022

  • Our minisymposium on methods for Data-driven modeling of unsteady fluid flows at the 19th USNCTAM was a big success! Jun 24, 2022

  • B. Herrmann enjoyed a productive visit to the McKeon group at Caltech! May 21, 2022

  • Paper just published in the Proceedings of the Royal Society A describes our new data-driven method—LANDO— that allows disambiguation between linear and nonlinear dynamics from measurementsApr 13, 2022

  • Our 2-part series of papers on gust mitigation control was published in PRF. Take a look at part I and part IIJan 10, 2022

  • We organized a seminar on data science for dynamical systems. Presentations are available hereDec 22, 2021

  • "Data-driven resolvent analysis" paper published in JFMMay 05, 2021

  • Communications Physics paper, "Modeling synchronization in forced turbulent oscillator flows", is now onlineOct 30, 2020

  • New preprint available online, where I show how to do resolvent analysis from data!  Oct 5, 2020

  • B. Herrmann gave a talk at the Second Symposium on Machine Learning and Dynamical Systems of the Fields Institute. Sep 21, 2020

  • Presented work on the synchronization dynamics of wake flows to the McKeon research group at Caltech. Mar 25, 2020