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SUMMARY:George Stepaniants (MIT)
DTSTART:20220310T223000Z
DTEND:20220311T000000Z
DTSTAMP:20260423T035413Z
UID:SPAMS/12
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/SPAMS/12/">L
 earning and predicting complex systems dynamics from single-variable obser
 vations</a>\nby George Stepaniants (MIT) as part of MIT Simple Person's Ap
 plied Mathematics Seminar\n\nLecture held in Room: 2 - 132 in the Simons B
 uilding.\n\nAbstract\nAdvances in model inference and data-driven science 
 have enabled the accurate discovery of governing equations from observatio
 ns alone\, accelerating our understanding and control of dynamical systems
 . However\, despite the ever-growing amount of experimental data collected
 \, many physical and biological systems can only be partially observed. He
 re\, building on recent progress in the inference and integration of nonli
 near differential equations\, we introduce an approach to learn a model us
 ing observations of just a single variable within a multi-variable dynamic
 al system\, and use this model to accurately predict future dynamics. Furt
 hermore\, we validate our approach on a variety of physical\, chemical and
  biological systems which exhibit nonlinear dynamics such as relaxation os
 cillations and limit cycles. This is joint work with Alasdair Hastewell\, 
 Dominic Skinner\, Jan Totz and Jörn Dunkel.\n
LOCATION:https://researchseminars.org/talk/SPAMS/12/
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