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    Epicycles, complex Fourier series and Homer Simpson's orbit

    Mathologer

    1 HN recommender · recommended in 3 months, 2022–2023

    YouTube

    25:35 · 421K views · Jul 6, 2018

    Mathologer explains epicycles and complex Fourier series, inspired by an animation of Homer Simpson drawn with epicycles. It covers the shape of the Moon's orbit around the Sun and how to make your own epicycle drawings.

    HN points to it as a visual teaching resource for Fourier series, and as a reference when epicycles come up as analogies.

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    3 comments from Hacker News

    1. @photochemsynNov 2022· in “Demystifying Fourier analysis”

      For an additional video exposition, I also like Eugene Khutoryansky's "Fourier Transform, Fourier Series, and frequency spectrum" [LINK] In particular, the visualization of the addition of sine waves of different frequencies in three dimensions is pretty spectacular. It's similar to Mathologer's "Epicycles, complex Fourier series and Homer Simpson's orbit": this video

      RecommendsView on HN
    2. @adolphJul 2023· in “1977 BBC Profile of Spirograph Inventor Denys Fisher”

      > replicate the elaborate machines that engrave currency I think you may refer to a rose engine, which in operation is very close to a dimensional spirograph. A modern variant might be a core-xy open etch-a-sketch using those LCD writing tablets and a set of physical gears and pins that would control the stylus' motion, maybe powered by a folding handcrank like the Panic Playdate. It might even be a tool for teaching the Fourier series. rose engine: [LINK] core-xy: [LINK] LCD writing tablet: [LINK] Panic Playdate: [LINK] Teaching Fourier series: this video Also, projects on Hackaday: [LINK]

      CitesView on HN
    3. @lisperApr 2022· in “Did the W-boson just “break the standard model”?”

      Falsifying supersymmetry is like falsifying Ptolemaic epicycles. You can never falsify it with data because epicycles can always be constructed to fit any data set [1]. Likewise, a sypersymmetric theory can be constructed to fit any set of parameters. But fitting the data is not enough. You have to fit the data with substantially fewer parameters than data points. That's the challenge. [1] this video

      CitesView on HN