Titlebook: Uncertainty Quantification in Scientific Computing; 10th IFIP WG 2.5 Wor Andrew M. Dienstfrey,Ronald F. Boisvert Conference proceedings 201

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Alexandru Cioaca,Adrian Sandu,Eric De Sturler,Emil Constantinescurs based on the bionic principle [152]. A controller that can be implemented online must be relatively simple in structure and have online adaptive and learning capabilities. The neural controllers are the preferred method to meet this requirement, especially recurrent neural networks. They not only

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Rafi L. Muhanna,Robert L. Mullenpproaches to synchronization in digital sys­ tem design are described and developed. We owe these techniques to a long history of effort in both digital system design and in digital communica­ tions, the latter field being relevant because large propagation delays have always been a dominant conside

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Wayne E. King,Athanasios Arsenlis,Charles Tong,William L. Oberkampfunified view of an otherwise seemingly heterogeneous field. It is widely recognized that a fundamental understanding of digital synchronization can only be reached by providing the designer with a solid theoretical framework, or else he will not know where to adjust his methods when he attempts to apply them 978-1-4899-1809-3978-1-4899-1807-9

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Uncertainty Quantification in Scientific Computing10th IFIP WG 2.5 Wor

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An Industrial Viewpoint on Uncertainty Quantification in Simulation: Stakes, Methods, Tools, Exampleplex system is described and forecast by a computer model, which is, most of time, deterministic. Yet, engineers coping with quantitative predictions using deterministic models deal actually with several sources of uncertainties affecting the inputs (and occasionally the model itself) which are tran

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Living with Uncertaintyary integral methods and other techniques, it has been possible to tailor the software to address specific issues that arise in painting processes applied to vehicles and to provide engineers with results for real-time optimization and manufacturing analysis. The title provides the focus and the pap

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Uncertainty and Sensitivity Analysis: From Regulatory Requirements to Conceptual Structure and Compuvity analyses is descibed. This approach is predicated on the description of the desired analysis in terms of three basic entities: (i) a probability space characterizing aleatory uncertainty, (ii) a probability space characterizing epistemic uncertainty, and (iii) a model that predicts system behav