Topic
Uncertainty quantification
About: Uncertainty quantification is a research topic. Over the lifetime, 8599 publications have been published within this topic receiving 132551 citations. The topic is also known as: UQ.
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TL;DR: The framework is demonstrated on a number, solving both the flow and adjoint systems of equations to provide a high-fidelity predictive capability and sensitivity information that can be used for optimal shape design using a gradient-based framework, goal-oriented adaptive mesh refinement, or uncertainty quantification.
Abstract: This paper presents the main objectives and a description of the SU2 suite, including the novel software architecture and open-source software engineering strategy. SU2 is a computational analysis and design package that has been developed to solve multiphysics analysis and optimization tasks using unstructured mesh topologies. Its unique architecture is well suited for extensibility to treat partial-differential-equation-based problems not initially envisioned. The common framework adopted enables the rapid implementation of new physics packages that can be tightly coupled to form a powerful ensemble of analysis tools to address complex problems facing many engineering communities. The framework is demonstrated on a number, solving both the flow and adjoint systems of equations to provide a high-fidelity predictive capability and sensitivity information that can be used for optimal shape design using a gradient-based framework, goal-oriented adaptive mesh refinement, or uncertainty quantification.
581 citations
Book•
01 May 1996
TL;DR: In this paper, the authors introduce Fault-Tree Construction and Fault Tree Construction, as well as other aspects of system analysis, such as human reliability, legal and regulatory risks, and uncertainty quantification.
Abstract: Preface. Basic Risk Concepts. Accident Mechanisms and Risk Management. Probabilistic Risk Assessment. Fault-Tree Construction. Qualitative Aspects of System Analysis. Quantification of Basic Events. Confidence Intervals. Quantitative Aspects of System Analysis. System Quantification for Dependent Events. Human Reliability. Uncertainty Quantification. Legal and Regulatory Risks. Index.
568 citations
TL;DR: This paper provides a methodology that incorporates the governing equations of the physical model in the loss/likelihood functions of the model predictive density and the reference conditional density as a minimization problem of the reverse Kullback-Leibler (KL) divergence.
560 citations
TL;DR: This paper describes the challenge problems and gives numerical values for the different input parameters so that results from different investigators can be directly compared and develop a better understanding of the relative advantages and disadvantages of traditional and newer methods.
548 citations
TL;DR: This approach achieves state of the art performance in terms of predictive accuracy and uncertainty quantification in comparison to other approaches in Bayesian neural networks as well as techniques that include Gaussian processes and ensemble methods even when the training data size is relatively small.
522 citations