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Dogs and Cats

Dogs and Cats. Dogs. Cats. Low-Dimensional Dogs and Cats: “Eigen-Dogs/Cats”. Linear Discrimination Analysis. Find a projection that maximizes the statistical distance between two random data sets . Histogram of the Data. 95% Accurate. Misclassified Dogs. It’s the ears!!!!.

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Dogs and Cats

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  1. Dogs and Cats Dogs Cats

  2. Low-Dimensional Dogs and Cats: “Eigen-Dogs/Cats”

  3. Linear Discrimination Analysis Find a projection that maximizes the statistical distance between two random data sets

  4. Histogram of the Data 95% Accurate

  5. Misclassified Dogs It’s the ears!!!!

  6. PDEsA backbone of AMATH (fluids) • Getting the right basis • - POD modes • Minimal dynamics • dynamical systems of the cheap

  7. How to solve a PDE: 3 easy steps 1 2 PDE ODE Algebra* 3 Undo • Eigenfunction expansion (separation of variables) • u(x,t) = Σan(t)ϕn(x)

  8. Two SolitonSolution of a nonlinear PDE

  9. SVD on Data

  10. Galerkin Expansion

  11. Nonlinear SchroedingerEquation

  12. Water Waves: Governing Equations V. E. Zakharov. “Stability of periodic waves of finite amplitude on the surface of a deep fluid.” Zh. Prikl. Mekh. Tekh. Fiz. (1968).

  13. Low Amplitude Waves

  14. High Amplitude Waves

  15. Mode Structure

  16. PCA (POD) Convergence The n mode POD basis of a set of data captures more of the L2normof that data than any other linear set of n modes: P. Holmes, J. Lumley, and G. Berkooz. Turbulence, Coherent Structures, Dynamical Systems and Symmetry. Cambridge U. Press (1996)

  17. www.mathworks/moler/eigs.pdf See also http://people.maths.ox.ac.uk/trefethen/text.html

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