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Why Wavelets?. Fourier analysis is inadequate it loses time localization for a given frequency.Fourier analysis is uneconomical still need infinite series even if function is almost flat.Fourier transform is unstable with respect to perturbation small changes in original function can complet
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1. Wavelets in Physics Paul Nerenberg
2. Why Wavelets? Fourier analysis is inadequate – it loses time localization for a given frequency.
Fourier analysis is uneconomical – still need infinite series even if function is almost flat.
Fourier transform is unstable with respect to perturbation – small changes in original function can completely modify the Fourier spectrum.
Because they’re fun, no doubt.
3. General Areas of Application Turbulence analysis – looking at fluid flows.
Astrophysics – image analysis and processing.
Systems of differential or polynomial equations (all physics) – wavelets can make solving them easier (it’s a deal, it’s a steal…).
All types of things I probably can’t even conceive of, but if it involves a signal, it can probably be analyzed with wavelets.
4. Multi-dimensional Wavelets Let’s take a brief look at the 2-D CWT…because I’m a physicist, this isn’t mathematically rigorous, but it’s still cool.
First, start out with s and s-hat for our 2-D space…
5. CWT in 2-D Only need to satisfy admissibility condition:
6. Choices of Wavelets Isotropic wavelets – good for pointwise analysis; pick a wavelet that is invariant under rotation; e.g. 2-D Mexican hat wavelet.
Anisotropic wavelets – detect directional features in an image/signal; support is contained in a convex cone; e.g. 2-D Morlet wavelet and Cauchy wavelets.
7. CWT in 3-D Let’s have some fun in 3-D…
8. Wavelets in Space-Time (don’t ask) Necessary for analysis of time-dependent signals…translations and dilations in space and time independently.
The interesting possibility here – relativistic wavelets, e.g. Poincaré wavelets, constrained within light cone, so useful for situations with EM fields.
9. Modeling Maxwell’s Equations and Magnetotellurics – A Specific Example Looking at solar wind-induced EM currents in the Earth’s magnetic core.
Core problem – a set of differential equations with boundary conditions (a ?12...different media) that needs to be solved with an iterative process.
10. Beyond our basic equations and boundary conditions… Make some approximations (physicists love them!)…so now we get two simple equations…
11. Looking at secondary component of the electric field… So electric field has two components – we want the secondary one…
12. Yet another formulation… Now we have:
13. Formulating it with Lagrangians…and the final iteration… Almost there…define a space too:
14. The Good News This result does converge…it’s actually worth something.
15. So what does this all mean…and why do we care at all? We took a set of differential problems, performed a domain decomposition, and then using a hybrid finite element method, found an iterative algorithm for solving these problems.
This reveals a core nature of wavelets – they can simplify extremely complex models – this is great for physics (or any area of applied science).