Error for perturbed solution vs Numeric

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Discussion Overview

The discussion revolves around the error analysis of a perturbed solution to a partial differential equation (PDE) compared to a numerical solution. Participants explore the implications of using a small parameter in an asymptotic expansion and the observed discrepancies in error measurements.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested

Main Points Raised

  • One participant describes their approach of using a naive expansion in a small parameter ##\epsilon## to approximate the analytic solution of a PDE, noting that the expected error should be ##O(\epsilon^2)##.
  • Another participant questions the choice of ##\epsilon=.9##, suggesting that it may not be sufficiently small to yield accurate results, and proposes testing with a smaller value, such as ##\epsilon=.1##.
  • A further reply indicates that even with a smaller ##\epsilon=0.01##, the error remains high at 0.165, while for ##\epsilon=0.2##, the error is slightly lower at 0.14, raising concerns about the validity of the perturbative approach.
  • Another participant suggests that the approximate solution may not be sufficiently close to the actual solution, implying a potential issue with the method used.

Areas of Agreement / Disagreement

Participants express differing views on the appropriateness of the chosen values for ##\epsilon## and the implications for error analysis. There is no consensus on the reasons for the observed errors or the effectiveness of the perturbative approach.

Contextual Notes

Participants have not resolved the assumptions regarding the size of ##\epsilon## and its impact on the accuracy of the perturbative solution. The discussion highlights the dependence on the choice of ##\epsilon## and the nature of the PDE being solved.

member 428835
Hi PF!

I'm solving an PDE where the analytic solution is called ##F(x)## (unknown). To approximate the analytic solution I made a naive expansion in some small parameter ##\epsilon## such that ##F(x) = f_0(x)+\epsilon f_1(x)+O(\epsilon^2)##, where I know ##f_0(x)## and ##f_1(x)##. I then solved the PDE numerically, let's call that solution ##F_n##. Then the error ##(F_n - (f_0(x)+\epsilon f_1(x)))/F_n## should be ##O(\epsilon^2)##. However, when I let ##\epsilon=.9## and then ##\epsilon=.8## my error is still about ##0.15##. How can this be?

I should say I know the numeric and asymptotic solutions are correct.
 
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joshmccraney said:
Hi PF!

I'm solving an PDE where the analytic solution is called ##F(x)## (unknown). To approximate the analytic solution I made a naive expansion in some small parameter ##\epsilon## such that ##F(x) = f_0(x)+\epsilon f_1(x)+O(\epsilon^2)##, where I know ##f_0(x)## and ##f_1(x)##. I then solved the PDE numerically, let's call that solution ##F_n##. Then the error ##(F_n - (f_0(x)+\epsilon f_1(x)))/F_n## should be ##O(\epsilon^2)##. However, when I let ##\epsilon=.9## and then ##\epsilon=.8## my error is still about ##0.15##. How can this be?

I should say I know the numeric and asymptotic solutions are correct.
##\epsilon=.9## isn't really all that small. Do you get better results for, say, ##\epsilon=.1##?
 
Mark44 said:
##\epsilon=.9## isn't really all that small. Do you get better results for, say, ##\epsilon=.1##?
So for ##\epsilon=0.01## I'm getting an error of 0.165 but for ##\epsilon=0.2## the error is 0.14. How could this ever be possible?
 
Maybe your approximate solution isn't that close to the actual solution...
 

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