Introduction: Uncertainty Quantification and Propagation
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Numerical modeling and simulation of complex systems is continuously developing at an incredible rate in many fields of engineering and sciences. This development has been made possible thanks to the constant evolution of numerical techniques and increasing availability of computational resources. Nowadays, simulations are essential tools for engineers throughout the design process. The simulations minimize the need for costly physical experiments that may be even impossible during early design stages. However, numerical simulations have to be carefully designed, performed, and verified, to yield useful and reliable information regarding the system being studied. In fact, the confidence one has in a computation is a key aspect when interpreting and analyzing simulations results. Indeed, simulations inherently involve errors, understanding and quantification of which is critical to assess the differences between the numerical predictions and actual system behavior.
KeywordsRandom Data Uncertainty Propagation Random Input Polynomial Chaos Polynomial Chaos Expansion
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