In some cases, common analysis tools and methods may not be sufficient for an engineer who wants to:
Let’s consider the situation where the goal is to design a building which is reliable with respect to a potential earthquake. The advanced design tools allow an engineer to build a model which predicts whether the building with the defined parameters (geometry, strength, and stiffness of the materials) will collapse or not in case of an earthquake with a certain magnitude, duration etc. However, in reality, the values of this predictive model parameters (geometry, strength, magnitude etc) always contain some uncertainty. This uncertainty can be both caused by technological limitations on the accuracy (in the context of geometry) and by the natural variability of a parameter (in the context of magnitude). So, while designing the building, the behavior of the model under consideration should be analyzed with respect to these uncertainties.
Specialists from a wide spectrum of industries such as energy, chemical and many other face the need to evaluate the influence of uncertain parameters of a product (material properties, operating conditions etc.) on the technical and operational characteristics guaranteed. An easy to use tool to fill this need is available in pSeven software package with the OpenTURNS algorithmic library integrated. Uncertainty Quantification with pSeven allows to significantly improve the quality of products designed, manage potential risks at the design, manufacturing and operating stages and to guarantee product reliability.
In pSeven, Uncertainty Quantification is performed by means of a full range of industryproven algorithms in an intuitive interface for users with no programming background, coupled with DATADVANCE’s proprietary optimization and approximation techniques of pSeven Core.
Uncertainty Quantification procedure in pSeven comprises three steps:
Steps 
Description 
Methods in pSeven 

I.  Computational model and criteria specification for further study  As an option to fasten the solution process, pSeven Core Approximation tool allows to replace computationally expensive models with the fast and accurate approximation models (surrogate models) 
To build an approximation model:

II.  Constructing a probabilistic model, describing the uncertainties of computational model parameters (quantification of the uncertainty sources)  There are two ways to construct a probabilistic model:


III.  Uncertainty propagation and reliability analysis  Identifying the characteristics of interest, assessment criteria (i.e. the distribution of the output of the computational model under uncertainties, the probability of failure). 

IV.  Additional step: Sensitivity Analysis  Identifying the influence of uncertainties of computational model parameters on the result of uncertainty propagation.
After this step is complete, the need to modify the probabilistic model may appear. 

V.  Uncertainty Quantification results visualization  Visualization of UM steps for performing preliminary data/model analysis and a better understanding of the obtained results  A wide variety of plot types available:

In pSeven the steps enumerated above are executed by the means of special blocks (workflow elements):