Fourier Analysis vs Topology: Which is More Useful for Physics?

In summary, the conversation discusses the choice between taking a Topology or Fourier Analysis course for an undergraduate physics major who is completing their Math minor. The general consensus is that Fourier Analysis would be more useful for physics, as it is necessary for solving PDEs and is a required course at some universities. However, some also argue that topology can have subtle but useful applications in physics, such as developing intuition for higher dimensions and understanding concepts like symplectic manifolds. Ultimately, the decision depends on individual goals and interests.
  • #1
ziggybenji
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0
Hey guys, long story short. I am completing my Math minor this semester and need to decide on whether Topology or Fourier Analysis. I am an undergraduate physics major and neither one of those classes is required for my B.S. in physics. So what do you guys think, Topology or Fourier Analysis?
 
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  • #2
Let me preface by saying i yet to take either course. However i think you definitively should go with Fourier analysis. It's incredible useful for physics since you need it to solve (a lot of) PDE. In fact at my university it's a required course for physics. Topology is afaik not very much used in physics and only come up in very specific research areas and the things in a math course on topology may not be the parts that's useful in physics.
 
  • #3
I'd lean towards Fourier analysis, but even as a disillusioned topologist, I can say their is some value in taking one topology course. In the grand scheme of things, one course isn't always going to matter that much. Topology can be useful in very subtle ways, even if it is not used explicitly. In your first topology course, you'll probably study point set topology, which I tend to think of as being almost the same subject as functional analysis (more relevant to physics), and historically, that was the way the subject developed. Topology builds your higher-dimensional intuition. If you want a deep understanding of physics, it's nice to be able to understand things like what a symplectic manifold is. The reason why Hamilton's equations aren't just meaningless symbols to me is that I know what a symplectic manifold is and Poisson brackets, the optical-mechanical analogy, and stuff like that. It's also nice to know things like covering spaces, and the fundamental group, since that sheds some light on the all-important double cover of SO(3) by SU(2). General relativity is another place where it would be helpful, more so if you go deeper into it (in my introductory GR class, the prof had to dance around the concept of a manifold because most of the class besides me didn't know what it was). It's not just in very specific research areas. Some basic topology is helpful in understanding the fundamentals of physics, just as long as you don't go overboard and become a topologist, like I did. Unless you want to be a string theorist or something, in which case, you'd be a bit crazy for wanting to be a string theorist. There are some other areas where knowing a bit more than the basics would be good, too--a physics student once showed me homotopy groups in some particle-theory book they were trying to read. But you could try to learn it on a more as-needed basis from "topology for physicists" books, like Nakhahara, later on, if you had to.

Fourier analysis is more fundamental, but I don't think topology would necessarily be a complete waste, depending on your goals and tastes. I'm not sure if you'd really need to know about Dirichlet kernels and Fejer kernels and delicate issues of convergence, much more than you would about topology, though. I think you probably come across what you need to know about Fourier series/transforms in the physics curriculum. As far as the more detailed stuff about Fourier series, I'd be a little wary of it if it isn't presented as well as Lanczos does in his book (Discourse on Fourier Series). It could easily become a tedious exercise in putting up with annoying technical garbage that would be mostly a waste of time. So, make sure to check out Lanczos from the library, just to make sure that doesn't happen.
 

Related to Fourier Analysis vs Topology: Which is More Useful for Physics?

1. What is topology?

Topology is a branch of mathematics that studies the properties of geometric spaces that are preserved under continuous deformations, such as stretching, twisting, or bending. It focuses on the spatial relationships between points and their neighborhoods, rather than the exact shape or size of the space itself.

2. What is Fourier analysis?

Fourier analysis is a mathematical technique used to decompose complex functions into simpler, periodic functions. It is based on the idea that any function can be represented as a sum of sine and cosine waves, and allows for the analysis of signals and data in terms of their frequency components.

3. How are topology and Fourier analysis related?

Topology and Fourier analysis are closely connected through the concept of continuous functions. Topological spaces are defined by their continuous properties, and Fourier analysis relies on the continuity of functions to decompose them into simpler components. In this way, topology provides a framework for understanding the continuity of functions that is essential in Fourier analysis.

4. What are some applications of topology and Fourier analysis?

Topology has many applications in fields such as physics, computer science, and engineering. It is used to study the properties of complex systems, model biological networks, and analyze data from various sources. Fourier analysis is used in signal processing, image and sound compression, and data analysis. It has also found applications in physics, engineering, and finance.

5. Are there any challenges or unsolved problems in topology or Fourier analysis?

Yes, there are still many open problems and challenges in both topology and Fourier analysis. Some of the major unsolved problems in topology include the Poincaré conjecture and the classification of 3-manifolds. In Fourier analysis, there are ongoing efforts to develop more efficient algorithms and techniques for data analysis, as well as to extend the theory to more general settings, such as non-Euclidean spaces.

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