Error propagation with two functions, two unknowns.

In summary, the author is asking if the equations are differentiable and if so, how can they find the derivatives.
  • #1
Hepth
Gold Member
464
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If I have two independent variables x,y, and two measurements, m1, m2 with errors. And the dependence is thus:
[tex]
m_1 \pm \delta m_1 = f[x,y]
[/tex]
[tex]
m_2 \pm \delta m_2 = g[x,y]
[/tex]

Now in my case, f and g are complicated expressions of x and y with no simple solution. (Actually I think i can solve one for x, but not for y).

Now if the equations were easy, I could solve for x and y:
[tex]
x \pm \delta_x = F[m_1, m_2,...]
[/tex]
[tex]
y \pm \delta_y = G[m_1, m_2,...]
[/tex]

And from there add the errors in quadrature to get the x and y errors.

BUT if I can't solve for x and y independently, and I must use numerical solutions to get the results ( I can, its easy). How can I go about getting the ERRORS? Is there another way I can solve for the errors and numerically solve for them, or a different method?

I have Mathematica if that helps.
 
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  • #2
You should explain what you mean by "error". Are you doing numerical approximations that involve an error in that sense? Are you doing a stochastic simulation where randomness causes an "error"? Or are you teking physical measurements with equipment that has a specified precision?
 
  • #3
Hepth said:
If I have two independent variables x,y, and two measurements, m1, m2 with errors. And the dependence is thus:
[tex]
m_1 \pm \delta m_1 = f[x,y]
[/tex]
[tex]
m_2 \pm \delta m_2 = g[x,y]
[/tex]

Now in my case, f and g are complicated expressions of x and y with no simple solution. (Actually I think i can solve one for x, but not for y).

Now if the equations were easy, I could solve for x and y:
[tex]
x \pm \delta_x = F[m_1, m_2,...]
[/tex]
[tex]
y \pm \delta_y = G[m_1, m_2,...]
[/tex]
Not sure if I've understood completely, but see if this helps.
Are f, g differentiable? Can you evaluate the derivatives at (m1, m2)? If so, can write [tex]\delta m_1 = \delta f = f_x \delta x + f_y \delta y; \delta m_2 = \delta g = g_x \delta x + g_y \delta y[/tex]
Evaluating fx etc. at (m1, m2), solve to find δx, δy.
Will need to check that the second order terms are not important.
 

Related to Error propagation with two functions, two unknowns.

What is error propagation?

Error propagation is a method used in scientific research to determine how uncertainties in measurements or calculations affect the final result. It involves analyzing the errors in each individual step of a process to determine the overall error in the final result.

How does error propagation work with two functions and two unknowns?

When dealing with two functions and two unknowns, error propagation involves calculating the partial derivatives of each function with respect to each unknown. These derivatives are then used to determine the overall error in the final result.

What is the formula for calculating error propagation with two functions and two unknowns?

The formula for error propagation with two functions and two unknowns is: error in final result = (partial derivative of function 1 with respect to unknown 1 * error in unknown 1)^2 + (partial derivative of function 1 with respect to unknown 2 * error in unknown 2)^2 + (partial derivative of function 2 with respect to unknown 1 * error in unknown 1)^2 + (partial derivative of function 2 with respect to unknown 2 * error in unknown 2)^2

What is the significance of error propagation in scientific research?

Error propagation is crucial in scientific research as it helps to quantify and understand the uncertainties in data and calculations. It allows scientists to determine the reliability and accuracy of their results, and make informed decisions about the validity of their findings.

Are there any limitations to error propagation with two functions and two unknowns?

Yes, there are limitations to error propagation with two functions and two unknowns. It assumes that the errors in each individual step of the process are independent and normally distributed. It also assumes that the errors in the final result are small compared to the values of the unknowns. Additionally, it may not take into account all sources of error and may not be applicable in all situations.

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