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On numerical solver selection and related uncertainty terminology
Author(s) -
Petra Claeys,
Ann van Griensven,
Lorenzo Benedetti,
Bernard De Baets,
Peter A. Vanrolleghem
Publication year - 2009
Publication title -
journal of hydroinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 50
eISSN - 1465-1734
pISSN - 1464-7141
DOI - 10.2166/hydro.2009.072
Subject(s) - terminology , solver , computer science , process (computing) , management science , selection (genetic algorithm) , problem solver , uncertainty quantification , risk analysis (engineering) , mathematical optimization , operations research , industrial engineering , mathematics , artificial intelligence , machine learning , engineering , software engineering , medicine , philosophy , linguistics , programming language , operating system
Mathematical models provide insight into numerous biological, physical and chemical systems. They can be used in process design, optimisation, control and decision support, as acknowledged in many different fields of scientific research. Mathematical models do not always yield reliable results and uncertainty should be taken into account. At present, it is possible to identify some factors contributing to uncertainty, and the awareness of the necessity of uncertainty assessment is rising. In the fields of Environmental Modelling and Computational Fluid Dynamics, for instance, terminology related to uncertainty exists and is generally accepted. However, the uncertainty due to the choice of the numerical solver and its settings used to compute the solution of the models did not receive much attention in the past. A motivating example on the existence and effect of numerical uncertainty is provided and clearly shows that we can no longer ignore it. This paper introduces a new terminology to support communication about uncertainty caused by numerical solvers, so that scientists become perceptive to it.

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