Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Uncertainty Quantification

Introduction to uncertainty quantification (overview)

  • Login to see the comments

  • Be the first to like this

Uncertainty Quantification

  1. 1. Uncertainty of modeling Most calculations relies upon more or less uncertain hypothesis about dependable parameters, initial and boundary values which typically describe geometrical dimensions, as well as physical parameters such as viscosity. The analysis that propagates these uncertainties to the modeling result often originates from perspectives and methods of mathematical statistics. The scientific field Uncertainty Quantification addresses this type of uncertainty of modeling. It is related to but not equivalent to mathematical statistics, as the latter gathers and infer statistical information of populations from finite sampling, while the former propagates known statistical information mathematically. Hence, a statistical analysis of repeated measurements often precedes UQ. Motivation The perceived quality, or uncertainty of modeling is of paramount importance for how we use the result. That is seldom explicitly stated, even though it is always the case. Without trust or confidence in the result it is literally speaking useless. The perceived quality might however differ substantially from the true quality – that is why it should be evaluated with credible UQ methods, rather than vaguely and subjectively guessed from experience. Modeling uncertainty may be utilized for decision making and risk assessment. For instance, evaluation of nuclear safety margins, road bridge design and forecasting of critical weather conditions with ensemble prognosis (Swedish) all rely upon our ability to correctly assess modeling uncertainty. It may be feasible with experimental verification of bridge strength by pulling and releasing an attached wire or loading with many heavy trucks. A similar test is not advisable though for safety critical nuclear applications and is not even possible for particular weather forecasts. Less critical but nevertheless important applications are development of products like, e.g. personal cars and heavy vehicles. Typically, a feasibility study suggests three possible versions and the task is to find 'the best', in order to establish a competitive edge. Simply relying upon precise numbers obtained from modeling, like fuel consumption, will suggest one only. Nevertheless, the performance of two versions may be indistinguishable considering the modeling uncertainty. If so, the result of UQ then suggest an experimental test instead of rejection based on modeling results. Otherwise, we might accidentally choose the second best version and loose against competitors which apply UQ more wisely.
  2. 2. Our approach The current prevailing practice is to evaluate modeling uncertainty with a method based on random sampling. It is also common to ignore the ambiguity implied by the ubiquitous incompleteness of our knowledge. This gives rise to a whole range of difficulties which substantially deteriorates the quality of the evaluated uncertainty. Our methodology is different and to a large extent based on our own research, see list of publications (in this repository). It is reflected against history and current practice in the strongly simplified illustration ”the right rope” (in this repository), constructed to be easily understood by anyone.