Open the Initial Condition Demo, select the "Sinusoid" initial condition, and choose "Even". Starting from
𝑛
=
1
, increase the number of Fourier terms one at a time. Choose the best answer to the following two questions:

A. Which parts of the initial condition show a good fit even with a small number of terms?

B. Which parts of the initial condition require more terms to fit well?

Question

Open the Initial Condition Demo, select the "Sinusoid" initial condition, and choose "Even". Starting from 
𝑛
=
1
, increase the number of Fourier terms one at a time. Choose the best answer to the following two questions:

A. Which parts of the initial condition show a good fit even with a small number of terms?

B. Which parts of the initial condition require more terms to fit well?

BlackTom AI · Accepted Answer

We begin by restating the scenario to ensure clarity: you open the Initial Condition Demo, pick the Sinusoid initial condition with the Even setting, then increment the Fourier terms starting from n=1, and you must reason about which regions of the initial condition are captured well with few terms versus those that need more terms.

Option A argues that the smoothly varying parts of the initial condition are captured well with a small number of Fourier terms. This aligns with Fourier theory: low-frequency components efficiently approximate gently varying regions, as smooth portions correspond to long-wavelength content which converge quickly when adding terms. In a sinusoidal, even-symmetric setup, the broad, gradual trends are represented by the first few harmonics, so these parts typically achieve a good fit early.

Option B contends that the discontinuities (ends and middle) require more terms to fit well. Discontinuities are classic sources of Gibbs phenomenon in Fourier approximations: sharp changes introduce high-frequency content that decays slowly, causing oscillations and poor local accuracy unless many terms are included. This means the ends and any sharp transitions within the initial condition will generally demand a larger n to reduce error and damp the overshoots.

To evaluate the distinction, consider the Fourier series behavior: smooth, slowly varying regions are dominated by the first few terms, producing a decent approximation with small n. In contrast, abrupt changes—like discontinuities or corners—inject high-frequency components that are not captured by early terms, so the representation improves gradually as you add more terms.

Additionally, think about symmetry: the Even setting enforces cosine terms, which naturally emphasize symmetric, smooth features of the waveform. Those symmetric, continuous sections will tend to be well-approximated with fewer harmonics, while any abrupt features within the middle or near the ends stand as the parts most in need of higher-order terms to reduce error and fill in the sharp transitions.

As you increase the number of Fourier terms one by one, you should observe that the regions with gentle variation reach good accuracy quickly, while the regions around discontinuities show persistent discrepancy and visible Gibbs-type ripples until a relatively larger number of terms is included.

In summary, the reasoning behind these statements rests on two well-established Fourier principles: (1) smooth, slowly varying portions are efficiently approximated by low-frequency components, hence a small n suffices; (2) discontinuities and sharp transitions introduce high-frequency content that requires many terms to approximate well, leading to slower convergence in those regions.

AMME2000 BMET2960 BMET9960 (ND) Week 4 Pre-work

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