During mathematical modeling of real technical system we can meet any type and rate model uncertainty. Its reasons can be incognizance of modelers or data inaccuracy. So, classification of uncertainties, with respect to they sources, distinguishes between aleatory and epistemic ones. The aleatory uncertainty is an inherent data variation associated with the investigated system or its environmen...

Information is indispensable in preparing economic decisions purposefully. In this paper knowledge is represented by a probability distribution. Knowledge acquisition is realized by the principle of maximum entropy which guarantees an unsophisticated expansion of knowledge and provides an unbiased basis for decision processes. Under a mathematical point of view entropy measures remaining uncert...

An overview of a comprehensive framework is given for estimating the predictive uncertainty of scientific computing applications. The framework is comprehensive in the sense that it treats both types of uncertainty (aleatory and epistemic), incorporates uncertainty due to the mathematical form of the model, and it provides a procedure for including estimates of numerical error in the predictive...

The numerical prediction of scramjet in-flight performance is a landmark example in which current simulation capability is overwhelmed by abundant uncertainty and error. The aim of this work is to develop a decision-making tool for balancing the available computational resources in order to equally reduce the effects of all sources of uncertainty and error below a confidence threshold. To that ...

In this study, we propose an efficient numerical simulation method for structural systems with both epistemic and aleatory uncertainties to evaluate the effect of epistemic uncertainty on the failure probability measured by variance-based sensitivity analysis. The direct evaluation of this effect requires a ‘‘triple-loop’’ crude sampling procedure, which is time consuming. To circumvent the dif...

Using online learning environments in higher education offers innovative possibilities to support collaborative learning. However, online learning creates new kinds of problems for participants who have not previously worked with each other. One of these problems is uncertainty which occurs when participants do not know each other. According to the uncertainty reduction theory, low uncertainty ...