Title:
Charting dynamics from data
Part 4: Data-driven dimension reduction, dynamic modeling, and control of complex chaotic systems
Charting dynamics from data
Part 4: Data-driven dimension reduction, dynamic modeling, and control of complex chaotic systems
Author(s)
Floryan, Daniel
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Abstract
We often find ourselves working with systems for which governing equations are unknown, or if they are known, they may be high-dimensional to the point of being difficult to analyze and prohibitively expensive to make predictions with. These difficulties, together with the ever-increasing availability of data, have led to the new paradigm of data-driven model discovery. I will present recent work that fruitfully combines a classical idea from applied mathematics with modern methods of machine learning to learn minimal dynamical models directly from time series data. In full analogy with cartography, we learn a representation of a system as an atlas of charts. This approach allows us to obtain dynamical models of the lowest possible dimension, leads to computational benefits, and can separate state space into regions of distinct behaviors. https://arxiv.org/abs/2108.05928
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Date Issued
2021-11-10
Extent
59:17 minutes
Resource Type
Moving Image
Resource Subtype
Lecture