Numerical differentiation of experimental data: local versus global methods

In the context of the analysis of measured data, one is often faced with the task to differentiate data numerically. Typically, this occurs when measured data are concerned or data are evaluated numerically during the evolution of partial or ordinary differential equations. Usually, one does not take care for accuracy of the resulting estimates of derivatives because modern computers are assumed to be accurate to many digits. But measurements yield intrinsic errors, which are often much less accurate than the limit of the machine used, and there exists the effect of “loss of significance”, well known in numerical mathematics and computational physics. The problem occurs primarily in numerical subtraction, and clearly, the estimation of derivatives involves the approximation of differences. In this article, we discuss several techniques for the estimation of derivatives. As a novel aspect, we divide into local and global methods, and explain the respective shortcomings. We have de...

• Type: Journal
• Year: 2007
• Where: CPHYSICS
• Authors: Karsten Ahnert, Markus Abel
• Keywords: CPHYSICS 2007, Global Methods, Intrinsic Errors, Numerically