Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
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This article presents an application of the least squares method in a particular class of inverse problems. Knowing the solution from experimental measurements, what are the corrections we must apply to the data of the problem in order to make the result close as much as possible to it? Normally the data of the problem are state functions known with a given degree of precision and in solving this problem the precision can be enhanced. This kind of problem is felt in many fields of engineering and physics, where an adjustment of a mathematical model on experimental observation is needed. This article shows a method to determine some residual function to be added to the data in order to refine the predictive power of the numerical model. An example is shown in a simple but concrete application.