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I am currently working with a physical system governed by $n$ number of parameters. Thus the functional form of the behaviour of this system over a length-scale $z$ can be represented by a non-linear function $F(x_1,x_2,...x_n,z)$. Now, this physical system is divided into 2 sub-systems such that: $F(x_1,x_2,...x_n,z)=\begin{cases} F_1(x_1,x_2,...x_n,z) & \text{if $z< z_0$} \\ F_2(x_1,x_2,...x_n,z) & \text{if $z\geq z_0$} \end{cases}$

Out of these $n$ parameters, $n-2$ are known and were obtained through fitting various sets of data obtained through numerical simulations. Thus, there is an associated error $\delta x_i$ for parameters $x_i (i=1,2,...,n-2)$. A set of data also yields the numerical values of $F_1$ and $F_2$ at $z=z_0$. Let's call these values $f_1^{z_0}$ and $f_2^{z_0}$. Note that there is a discontinuity at $z_0$. These numerical values can be used to find the 2 remaining unknown parameters $x_{n-1}$ and $x_n$. For clarity, let's call them $y_1$ and $y_2$. Now the problem boils down to solving the following system of non-linear equations for $y_1$ and $y_2$:

$F_1(x_1,x_2,...x_{n-2},z_0,\mathbf{y_1},\mathbf{y_2})=f_1^{z_0}$

$F_2(x_1,x_2,...x_{n-2},z_0,\mathbf{y_1},\mathbf{y_2})=f_2^{z_0}$

This can be done easily by using fsolve in Matlab or FindRoot in Mathematica, and I obtain unique solutions for the 2 unknowns. However, I need to determine the error in the estimation of $y_1$ and $y_2$, as a result of error propagation from $x_1,x_2,...x_{n-2},f_1^{z_0},f_1^{z_0}$.

Does someone know of a systematic way to find $\delta y_1$ and $\delta y_2$?

So far I have tried the following 2 approaches:

  1. A very crude method: Solve the pair of non-linear equations for combinations of 3 different values of each known parameter: $\{x_i-\delta x_i,x_i,x_i+\delta x_i\}$ and $\{f_1^{z_0}-\delta f_1^{z_0},f_1^{z_0},f_1^{z_0}+\delta f_1^{z_0}\}$, $\{f_2^{z_0}-\delta f_2^{z_0},f_2^{z_0},f_2^{z_0}+\delta f_2^{z_0}\}$. By doing so, I obtain $3^n$ sets of solutions for $y_1$ and $y_2$. Then I treat the $max\{y_1\}$ and $max\{y_2\}$ as my error bounds. I recently realised that the error propagation obtained in this way might be an overestimation.

  2. Let $G_1=F_1(x_1,x_2,...x_{n-2},z_0,\mathbf{y_1},\mathbf{y_2})-f_1^{z_0}$ and $G_2=F_2(x_1,x_2,...x_{n-2},z_0,\mathbf{y_1},\mathbf{y_2})-f_2^{z_0}$. LEt the error for these functions be: $\delta G_1$ and $\delta G_2$. Now we simultaneously solve for $y_1$ and $y_2$ such that $G_1=0$ and $G_2=0$, $\implies$$\delta G_1=0$ and $\delta G_2=0$. Expanding the right-hand side of these equations using Taylor expansion up to first order by assuming that the errors are small enough, we get:

$\sum_{i=1}^{n-2}\frac{\partial G_1}{\partial x_i} \delta x_i+\frac{\partial G_1}{\partial f_1^{z_0}}\delta f_1^{z_0}+\frac{\partial G_1}{\partial y_1} \delta y_1+\frac{\partial G_1}{\partial y_2} \delta y_2=0$

$\sum_{i=1}^{n-2}\frac{\partial G_2}{\partial x_i} \delta x_i+\frac{\partial G_2}{\partial f_1^{z_0}}\delta f_1^{z_0}+\frac{\partial G_2}{\partial y_1} \delta y_1+\frac{\partial G_2}{\partial y_2} \delta y_2=0$

Then we can substitute the values of known parameters, their errors and solutions for $y_1$ and $y_2$ obtained by solving $G_1=0$ and $G_2=0$ in the above 2 equations we can find solutions for $\delta y_1$ and $\delta y_2$.

I have a feeling that this method assumes that the extraction of errors for all parameters is equivalent, which is not the case for $y_1$ and $y_2$, since they are evaluated using root-finding methods.

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closed as off-topic by sammy gerbil, Yashas, Kyle Kanos, Jon Custer, David Hammen Mar 11 '17 at 11:42

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  • $\begingroup$ Well, how did you find the error of the other parameters? Wasn't that a sort of root finding as well? $\endgroup$ – mikuszefski Mar 10 '17 at 14:57
  • $\begingroup$ I found the errors on other parameters by fitting my data to different independent functions dependent on the individual parameters. Of course, non-linear least squares fitting is similar to root-finding, but there I had $m$ data-points (observations) and 1 unknown $(m>1)$ and the error is found in terms of the residual of the function. But now, we want to fit 2 data points $f_1^{z_0}$ and $f_2^{z_0}$ to 2 functions $F_1$ and $F_2$, in order to find $y_1$ and $y_2$. Thus, the residual will simply be the machine precision, or whatever accuracy I choose. $\endgroup$ – User1305 Mar 10 '17 at 16:17
  • $\begingroup$ What I am looking for is an error estimate for $y_1$ and $y_2$ subject to uncertainties in all other parameters. $\endgroup$ – User1305 Mar 10 '17 at 16:19
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    $\begingroup$ I'm voting to close this question as off-topic because this is a technical mathematical question with no physics content, and would be better on Mathematics SE or Cross-Validated SE. $\endgroup$ – sammy gerbil Mar 10 '17 at 17:38
  • $\begingroup$ @sammygerbil While Cross-Validated SE might be the right place, one has too keep in mind: Error estimation, handling and propagation, concepts of errors, error handling, and statistics are of fundamental importance in experimental physics. $\endgroup$ – mikuszefski Mar 13 '17 at 6:54