Consistency Versus Convergence, seeking intuition

In summary, consistency refers to the error of the real solution in a finite difference scheme going to zero as the time step goes to zero. This is different from convergence, which means that the solution calculated by the finite difference scheme will converge to the real solution of the partial differential equation. Consistency applies to partial differential operators, while convergence applies to partial differential equations. The operator produces a true solution, but the finite difference scheme produces an approximation. Therefore, consistency means that the difference between the operator and the finite difference scheme approaches zero, while convergence means that the difference between the real solution and the finite difference solution approaches zero.
  • #1
fahraynk
186
6
TL;DR Summary
Trying to understand the difference between consistency and convergence in PDE finite difference methods, and why we need both definitions. Why they do not really both mean the same thing.
What is the definition of consistency?

I have seen a proof that shows a finite difference scheme is consistent, where they basically plug a true solution ##𝑢(𝑡)##
into a finite difference scheme, and they get every term, for example ##𝑢^{𝑖+1}_𝑗## and ##𝑢^𝑖_{𝑗+1}##, using taylors polynomials.

Then, they show that the taylor approximations plugged into the finite difference scheme go to zero as Δ𝑡

goes to zero.

So this seems like the definition should be, Consistency : The error of the real solution in the finite difference scheme goes to zero as time step goes to zero.

So, on the difference between convergence and consistency, it seems like convergence is computing 𝑢(𝑡+Δ𝑡)
with the finite difference scheme and having a low error, while consistency is plugging the true values of 𝑢 and 𝑢(𝑡+Δ𝑡)
into the finite difference scheme and having a low error.

But... they are kind of exactly the same, since if the finite difference scheme is convergent, the approximation for 𝑢(𝑡+Δ𝑡)
converges to the true value, and so if you plug the true value into the fin. dif. equation or you plug the approximation in, why would the output be different?

Can anyone give me some intuition?
 
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  • #2
I think convergence means that using the finite difference scheme your solution will converge to a real solution. That's what you thought.

However, consistency does not apply to partial differential equations, but to partial differential operators. The finite difference scheme is consistent with the partial differential equation if, for any smooth function, the value calculated by the finite difference scheme converges to the real value calculated by the operator. Look for convergence on the following page.
 
  • #3
Thank you so much for the reply,

Periwinkle said:
I think convergence means that using the finite difference scheme your solution will converge to a real solution. That's what you thought.

However, consistency does not apply to partial differential equations, but to partial differential operators. The finite difference scheme is consistent with the partial differential equation if, for any smooth function, the value calculated by the finite difference scheme converges to the real value calculated by the operator. Look for convergence on the following page.

Whats the difference between a partial differential operator and a PDE?
Isn't an operator, say, ##L##, such that ##Lu=u_{tt}+u_{xx}+u_{xt}+...## ##(meaning Lu## equals the pde?)

The operator produces a true solution right? So ##Lu=u^{n+1}## where ##n## is time. So, basically saying that ##Lu## converges to the finite difference scheme (call it ##FD(u)##) means that ##|(L-FD)u|\rightarrow 0## right? BUT ##Lu## is the true solution and ##FD(u)## is the finite difference solution. So.. why is this different than convergence? ##|L(u)-FD(u)|\rightarrow 0## is convergence right?
 

1. What is consistency versus convergence?

Consistency versus convergence is a concept in science and mathematics that refers to the balance between accuracy and precision in measurements. Consistency refers to the repeatability of measurements, while convergence refers to the closeness of measurements to the true value.

2. Why is consistency versus convergence important in scientific research?

Consistency versus convergence is important because it helps scientists evaluate the reliability and validity of their data. It also allows for the identification of any systematic errors in the measurement process, which can be corrected to improve the accuracy of results.

3. How do scientists determine the consistency and convergence of their data?

Scientists use statistical analysis and mathematical models to assess the consistency and convergence of their data. This involves comparing multiple measurements and calculating measures of central tendency and variability to determine the level of agreement between them.

4. What are some potential sources of inconsistency or lack of convergence in scientific data?

Some potential sources of inconsistency or lack of convergence in scientific data include human error, faulty equipment or instruments, and environmental factors that may affect the measurement process. It is important for scientists to carefully control and account for these factors in their research.

5. How can scientists improve consistency and convergence in their data?

Scientists can improve consistency and convergence in their data by using standardized and calibrated equipment, carefully controlling experimental conditions, and repeating measurements multiple times. Additionally, using more advanced statistical techniques and larger sample sizes can also help improve the accuracy and precision of measurements.

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