Nonlinear Dynamics, High Dimensional Data, and Persistent Homology

Explore the intersection of nonlinear dynamics, high-dimensional data, and persistent homology in this comprehensive lecture by Konstantin Mischaikow. Delve into various forms of data, including point clouds and experimental data, and understand their applications in quasistatic systems and numerical simulations. Discover the principles of algebraic topology and updown symmetry before diving into persistent homology, persistence diagrams, and associated metrics. Examine recent developments in the field and potential future directions. Gain insights into persistent homology point clouds, velocity profiles, and periodic orbits. Conclude by understanding the end goals of this cutting-edge research in applied algebraic topology.

Intro
Two forms of data
Point clouds
Experimental Data
Quasistatic
Numerical Simulations
Algebraic Topology
Updown Symmetry
Persistent Homology
Persistence Diagrams
Metrics
Recent Work
Next Steps
Persistent Homology Point Cloud
Persistent Homology Points
Velocity Profiles
Periodic Orbit
End Goals