Uncertainty Propagation of Complex Functions

In summary, the conversation discusses the calculation of uncertainty for a function with both real and complex parts, specifically in the case of eigenvalues of a coupled oscillator. The question arises on how to do standard error propagation when dealing with imaginary numbers and whether to use the real part of the function instead. It is also mentioned that some physical systems are better represented by complex numbers, making it important to consider the distribution of the complex number and the function.
  • #1
HermitianField
8
0
Suppose I have some observables [itex] \alpha, \beta, \gamma[/itex] whose central values and uncertainties [itex]\sigma_{\alpha}, \sigma_{\beta}, \sigma_{\gamma} [/itex] are known.

Define a function [itex] f(\alpha, \beta, \gamma)[/itex] which has both real and complex parts. How do I do standard error propagation when imaginary numbers are involved? This problem deals with the eigenvalues of a coupled oscillator. Here, some of the eigenvalue functions are complex, so I would like to know how to calculate the uncertainty on f, which is an eigenvalue. The claim is that since coupled oscillators are physical systems, their answers are real in nature. Thus, should I use [itex] Re(f) [/itex] instead?
 
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  • #2
Some physical systems are better represented by complex numbers than real numbers. So simply being a physical system doesn't imply the measurement is a real number.

The better question is what is the distribution of the complex number and the function.
 

Related to Uncertainty Propagation of Complex Functions

1. What is uncertainty propagation of complex functions?

Uncertainty propagation of complex functions is a mathematical method for estimating the uncertainty in the output of a complex function based on the uncertainties in its input variables.

2. Why is uncertainty propagation important in scientific research?

Uncertainty propagation is important because it allows scientists to understand and quantify the uncertainty in their measurements and calculations, which is crucial for making accurate and reliable conclusions from their data.

3. How is uncertainty propagation different from error propagation?

Uncertainty propagation takes into account both random errors and systematic errors, while error propagation only considers random errors. Additionally, uncertainty propagation uses statistical methods to estimate the uncertainty, while error propagation relies on mathematical formulas.

4. What are the limitations of uncertainty propagation?

Uncertainty propagation assumes that the input variables are independent and normally distributed, which may not always be the case in real-world situations. Additionally, it can become computationally intensive for complex functions with many input variables.

5. How can uncertainty propagation be applied in practical situations?

Uncertainty propagation can be applied in various fields, such as physics, chemistry, and engineering, to estimate the uncertainties in measurements and calculations. It can also be used in risk analysis and decision making processes to assess the potential impact of uncertainties.

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