NPL is active in areas of mathematics and scientific computing which support measurement science, with research work carried out in the Mathematics & Modelling for Metrology (MMM).
All measurements are subject to uncertainty and a measured value is meaningless without a quantitative statement of its quality in the form of an uncertainty. NPL develops techniques and analysis methods to help ensure that uncertainties quoted are defensible. In particular, the application of Bayesian statistics and Monte Carlo methods to uncertainty evaluation are currently being studied. NPL plays in large role in the development of the Guide to the expression of uncertainty in Measurement (GUM) and its supplements as well as the analysis of data collected in international key comparisons of measurements made by National Metrology Institutes.
Mathematical modelling, from the macro- to the nano-scale, is widely used in science and engineering to predict the behaviour of components, systems and experiments. In many cases, the model relies on experimental data which is itself subject to measurement uncertainty. NPL is active in a range of projects with the general aim of assessing the reliability of modelling software packages and thereby improving users' confidence in the results they obtain. Case studies across all areas of metrology are undertaken to improve the state of the art and to develop best practice which is disseminated through publications and reports.