Abstract
Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in many fields and has been highly valued and strongly supported by academia and industry. This review firstly discusses the sources and the types of uncertainties and gives an overall discussion on the goal, practical significance, and basic framework of the research of UQ. Then, the core ideas and typical methods of several important UQ processes are introduced, including sensitivity analysis, uncertainty propagation, model calibration, Bayesian inference, experimental design, surrogate model, and model uncertainty analysis.
Highlights
In weapon manufacturing [1], aerodynamics modeling [2], detonation modeling [3], inertial confinement fusion [4], and other natural science and engineering fields, test, modeling, and simulation are three main approaches to understand a complex process. e basic element of scientific exploration is experiment and observation
In the complex physical and engineering problems, modeling and simulation (M&S) research has gradually developed a mature system, and encountered new challenges. e uncertainties introduced by many different sources become a major obstacle for the predictive capability and the reliability of simulations. e research of uncertainty quantification (UQ) aims to make better decisions, reduce the cost of trial and error during code development, and improve the reliability of simulation through identifying the main source of uncertainty, analyzing how the uncertainty propagates, searching for stable optimized solutions, and so on
According to the data flow, the research of UQ can be divided into two categories: forward analysis concerning about the uncertainty propagation from input to output and backward analysis concerning about how to obtain the inference of input from experiment data and simulation output
Summary
In weapon manufacturing [1], aerodynamics modeling [2], detonation modeling [3], inertial confinement fusion [4], and other natural science and engineering fields, test, modeling, and simulation are three main approaches to understand a complex process. e basic element of scientific exploration is experiment and observation. E importance of simulation is that it allows parameters to be changed in the models to understand the cause and effect of some complex phenomena which might be too expensive or dangerous for conventional experimental methods [8]. In these processes, many uncertainties are introduced. It is important to quantify the errors in order to be able to interpret the results [9], including identifying the main sources of uncertainty, analyzing how the uncertainty propagates in complex systems, and finding stable optimized solutions across a wide range of inputs, and make better decisions at a known level of confidence, so as to reduce development time, prototype cost, and unexpected failure. Is paper will give the basic framework of research on UQ and systematically review the core ideas and typical methods of several important UQ processes
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