Статті в журналах: "Uncertainly quantification"

1

Jalaian, Brian, Michael Lee, and Stephen Russell. "Uncertain Context: Uncertainty Quantification in Machine Learning." AI Magazine 40, no. 4 (December 20, 2019): 40–49. http://dx.doi.org/10.1609/aimag.v40i4.4812.

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Machine learning and artificial intelligence will be deeply embedded in the intelligent systems humans use to automate tasking, optimize planning, and support decision-making. However, many of these methods can be challenged by dynamic computational contexts, resulting in uncertainty in prediction errors and overall system outputs. Therefore, it will be increasingly important for uncertainties in underlying learning-related computer models to be quantified and communicated. The goal of this article is to provide an accessible overview of computational context and its relationship to uncertainty quantification for machine learning, as well as to provide general suggestions on how to implement uncertainty quantification when doing statistical learning. Specifically, we will discuss the challenge of quantifying uncertainty in predictions using popular machine learning models. We present several sources of uncertainty and their implications on statistical models and subsequent machine learning predictions.

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2

Verdonck, H., O. Hach, J. D. Polman, O. Braun, C. Balzani, S. Müller, and J. Rieke. "-An open-source framework for the uncertainty quantification of aeroelastic wind turbine simulation tools." Journal of Physics: Conference Series 2265, no. 4 (May 1, 2022): 042039. http://dx.doi.org/10.1088/1742-6596/2265/4/042039.

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Abstract The uncertainty quantification of aeroelastic wind turbine simulations is an active research topic. This paper presents a dedicated, open-source framework for this purpose. The framework is built around the uncertainpy package, likewise available as open source. Uncertainty quantification is done with a non-intrusive, global and variance-based surrogate model, using PCE (i.e., polynomial chaos expansion). Two methods to handle the uncertain parameter distribution along the blades are presented. The framework is demonstrated on the basis of an aeroelastic stability analysis. A sensitivity analysis is performed on the influence of the flapwise, edgewise and torsional stiffness of the blades on the damping of the most critical mode for both a Bladed linearization and a Bladed time domain simulation. The sensitivities of both models are in excellent agreement and the PCE surrogate models are shown to be accurate approximations of the true models.

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3

Cacuci, Dan Gabriel. "Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems." Energies 15, no. 17 (September 1, 2022): 6379. http://dx.doi.org/10.3390/en15176379.

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The Special Issue “Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems” comprises nine articles that present important applications of concepts for performing sensitivity analyses and uncertainty quantifications of models of nuclear energy systems [...]

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4

Oh, Deog Yeon, Young Seok Bang, Kwang Won Seul, and Sweng Woong Woo. "ICONE23-1466 UNCERTAINTY QUANTIFICATION OF PHYSICAL MODELS USING CIRCE METHOD." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2015.23 (2015): _ICONE23–1—_ICONE23–1. http://dx.doi.org/10.1299/jsmeicone.2015.23._icone23-1_213.

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Hu, Juxi, Lei Wang, and Xiaojun Wang. "Non-Probabilistic Uncertainty Quantification of Fiber-Reinforced Composite Laminate Based on Micro- and Macro-Mechanical Analysis." Applied Sciences 12, no. 22 (November 18, 2022): 11739. http://dx.doi.org/10.3390/app122211739.

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In this paper, the main aim is to study and predict macro elastic mechanical parameters of fiber-reinforced composite laminates by combining micro-mechanical analysis models and the non-probabilistic set theory. It deals with uncertain input parameters existing in quantification models as interval variables. Here, several kinds of micro-mechanical mathematical models are introduced, and the parameter vertex solution theorem and the Monte Carlo simulation method can be used to perform uncertainty quantification of macro elastic properties for composites. In order to take the correlations between macro elastic properties into consideration, the obtained limited sample points or experimental data are utilized on the basis of the grey mathematical modeling theory, where correlated uncertain macro parameters can be treated as a closed and bounded convex polyhedral model. It can give out a clear analytical description for feasible domains of correlated macro elastic properties in the process of uncertainty quantification. Finally, two numerical examples are carried out to account for the validity and feasibility of the proposed quantification method. The results show that the proposed method can become a powerful and meaningful supplement for uncertainty quantification of composite laminates and provide data support for further uncertainty propagation analysis.

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6

Sun, X., T. Kirchdoerfer, and M. Ortiz. "Rigorous uncertainty quantification and design with uncertain material models." International Journal of Impact Engineering 136 (February 2020): 103418. http://dx.doi.org/10.1016/j.ijimpeng.2019.103418.

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7

Cheng, Xi, Clément Henry, Francesco P. Andriulli, Christian Person, and Joe Wiart. "A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data." International Journal of Environmental Research and Public Health 17, no. 7 (April 9, 2020): 2586. http://dx.doi.org/10.3390/ijerph17072586.

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This paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the patient’s scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.

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8

Ernst, Oliver, Fabio Nobile, Claudia Schillings, and Tim Sullivan. "Uncertainty Quantification." Oberwolfach Reports 16, no. 1 (February 26, 2020): 695–772. http://dx.doi.org/10.4171/owr/2019/12.

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9

Salehghaffari, S., and M. Rais-Rohani. "Material model uncertainty quantification using evidence theory." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 10 (January 8, 2013): 2165–81. http://dx.doi.org/10.1177/0954406212473390.

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Uncertainties in material models and their influence on structural behavior and reliability are important considerations in analysis and design of structures. In this article, a methodology based on the evidence theory is presented for uncertainty quantification of constitutive models. The proposed methodology is applied to Johnson–Cook plasticity model while considering various sources of uncertainty emanating from experimental stress–strain data as well as method of fitting the model constants and representation of the nondimensional temperature. All uncertain parameters are represented in interval form. Rules for agreement, conflict, and ignorance relationships in the data are discussed and subsequently used to construct a belief structure for each uncertain material parameter. The material model uncertainties are propagated through nonlinear crush simulation of an aluminium alloy 6061-T6 circular tube under axial impact load. Surrogate modeling and global optimization techniques are used for efficient calculation of the propagated belief structure of the tube response, whereas Yager’s aggregation rule of evidence is used for multi-model consideration. Evidence-based uncertainty in the structural response is measured and presented in terms of belief, plausibility, and plausibility-decision values.

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10

Tuczyński, Tomasz, and Jerzy Stopa. "Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm." Energies 16, no. 3 (January 20, 2023): 1153. http://dx.doi.org/10.3390/en16031153.

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Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm’s efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir.

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11

Guo, Xianpeng, Dezhi Wang, Lilun Zhang, Yongxian Wang, Wenbin Xiao, and Xinghua Cheng. "Uncertainty Quantification of Underwater Sound Propagation Loss Integrated with Kriging Surrogate Model." International Journal of Signal Processing Systems 5, no. 4 (December 2017): 141–45. http://dx.doi.org/10.18178/ijsps.5.4.141-145.

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Liu, Chang, and Duane A. McVay. "Continuous Reservoir-Simulation-Model Updating and Forecasting Improves Uncertainty Quantification." SPE Reservoir Evaluation & Engineering 13, no. 04 (August 12, 2010): 626–37. http://dx.doi.org/10.2118/119197-pa.

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Summary Most reservoir-simulation studies are conducted in a static context—at a single point in time using a fixed set of historical data for history matching. Time and budget constraints usually result in significant reduction in the number of uncertain parameters and incomplete exploration of the parameter space, which results in underestimation of forecast uncertainty and less-than-optimal decision making. Markov Chain Monte Carlo (MCMC) methods have been used in static studies for rigorous exploration of the parameter space for quantification of forecast uncertainty, but these methods suffer from long burn-in times and many required runs for chain stabilization. In this paper, we apply the MCMC in a real-time reservoirmodeling application. The system operates in a continuous process of data acquisition, model calibration, forecasting, and uncertainty quantification. The system was validated on the PUNQ (production forecasting with uncertainty quantification) synthetic reservoir in a simulated multiyear continuous-modeling scenario, and it yielded probabilistic forecasts that narrowed with time. Once the continuous MCMC simulation process has been established sufficiently, the continuous approach usually allows generation of a reasonable probabilistic forecast at a particular point in time with many fewer models than the traditional application of the MCMC method in a one-time, static simulation study starting at the same time. Operating continuously over the many years of typical reservoir life, many more realizations can be run than with traditional approaches. This allows more-thorough investigation of the parameter space and more-complete quantification of forecast uncertainty. More importantly, the approach provides a mechanism for, and can thus encourage, calibration of uncertainty estimates over time. Greater investigation of the uncertain parameter space and calibration of uncertainty estimates by using a continuous modeling process should improve the reliability of probabilistic forecasts significantly.

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13

Caldeira, João, and Brian Nord. "Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms." Machine Learning: Science and Technology 2, no. 1 (December 4, 2020): 015002. http://dx.doi.org/10.1088/2632-2153/aba6f3.

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14

Rajaraman, Sivaramakrishnan, Ghada Zamzmi, Feng Yang, Zhiyun Xue, Stefan Jaeger, and Sameer K. Antani. "Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays." Biomedicines 10, no. 6 (June 4, 2022): 1323. http://dx.doi.org/10.3390/biomedicines10061323.

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Deep learning (DL) methods have demonstrated superior performance in medical image segmentation tasks. However, selecting a loss function that conforms to the data characteristics is critical for optimal performance. Further, the direct use of traditional DL models does not provide a measure of uncertainty in predictions. Even high-quality automated predictions for medical diagnostic applications demand uncertainty quantification to gain user trust. In this study, we aim to investigate the benefits of (i) selecting an appropriate loss function and (ii) quantifying uncertainty in predictions using a VGG16-based-U-Net model with the Monto–Carlo (MCD) Dropout method for segmenting Tuberculosis (TB)-consistent findings in frontal chest X-rays (CXRs). We determine an optimal uncertainty threshold based on several uncertainty-related metrics. This threshold is used to select and refer highly uncertain cases to an expert. Experimental results demonstrate that (i) the model trained with a modified Focal Tversky loss function delivered superior segmentation performance (mean average precision (mAP): 0.5710, 95% confidence interval (CI): (0.4021,0.7399)), (ii) the model with 30 MC forward passes during inference further improved and stabilized performance (mAP: 0.5721, 95% CI: (0.4032,0.7410), and (iii) an uncertainty threshold of 0.7 is observed to be optimal to refer highly uncertain cases.

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15

Owhadi, H., C. Scovel, T. J. Sullivan, M. McKerns, and M. Ortiz. "Optimal Uncertainty Quantification." SIAM Review 55, no. 2 (January 2013): 271–345. http://dx.doi.org/10.1137/10080782x.

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16

Xu, Ting. "Uncertainty, Ignorance and Decision-Making." Amicus Curiae 3, no. 1 (October 27, 2021): 10–32. http://dx.doi.org/10.14296/ac.v3i1.5350.

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A great deal of decision-making during crises is about coping with uncertainty. For rulemakers, this poses a fundamental challenge, as there has been a lack of a rigorous framework for understanding and analysing the nature and function of uncertainty in the context of rulemaking. In coping with crises, modelling has become a governance tool to navigate and tame uncertainty and justify decisions. This is because models, in particular mathematical models, can be useful to produce precise answers in numbers. This article examines the challenges rulemakers are facing in an uncertain world and argues that one of the most important challenges lies in rulemakers’ failures to understand the nature of uncertainty and ignorance in the contested arena of science for decision-making. It focuses on the relationship between uncertainty, ignorance and decisionmaking through a case study of the interaction between modelling and rulemaking in the Covid-19 pandemic. In so doing, this article provides an alternative strategy to number- and model-based rulemaking in an uncertain world. It provokes a rethinking of using science to measure and govern human affairs and the impact of numbers and quantification on law. Keywords: uncertainty; ignorance; decision-making; rulemaking; models; mathematical modelling; quantification; Covid-19.

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17

Bin, Junchi, Ran Zhang, Rui Wang, Yue Cao, Yufeng Zheng, Erik Blasch, and Zheng Liu. "An Efficient and Uncertainty-Aware Decision Support System for Disaster Response Using Aerial Imagery." Sensors 22, no. 19 (September 21, 2022): 7167. http://dx.doi.org/10.3390/s22197167.

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Efficient and robust search and rescue actions are always required when natural or technical disasters occur. Empowered by remote sensing techniques, building damage assessment can be achieved by fusing aerial images of pre- and post-disaster environments through computational models. Existing methods pay over-attention to assessment accuracy without considering model efficiency and uncertainty quantification in such a life-critical application. Thus, this article proposes an efficient and uncertain-aware decision support system (EUDSS) that evolves the recent computational models into an efficient decision support system, realizing the uncertainty during building damage assessment (BDA). Specifically, a new efficient and uncertain-aware BDA integrates the recent advances in computational models such as Fourier attention and Monte Carlo Dropout for uncertainty quantification efficiently. Meanwhile, a robust operation (RO) procedure is designed to invite experts for manual reviews if the uncertainty is high due to external factors such as cloud clutter and poor illumination. This procedure can prevent rescue teams from missing damaged houses during operations. The effectiveness of the proposed system is demonstrated on a public dataset from both quantitative and qualitative perspectives. The solution won the first place award in International Overhead Imagery Hackathon.

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18

Herty, Michael, and Elisa Iacomini. "Uncertainty quantification in hierarchical vehicular flow models." Kinetic and Related Models 15, no. 2 (2022): 239. http://dx.doi.org/10.3934/krm.2022006.

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<p style='text-indent:20px;'>We consider kinetic vehicular traffic flow models of BGK type [<xref ref-type="bibr" rid="b24">24</xref>]. Considering different spatial and temporal scales, those models allow to derive a hierarchy of traffic models including a hydrodynamic description. In this paper, the kinetic BGK–model is extended by introducing a parametric stochastic variable to describe possible uncertainty in traffic. The interplay of uncertainty with the given model hierarchy is studied in detail. Theoretical results on consistent formulations of the stochastic differential equations on the hydrodynamic level are given. The effect of the possibly negative diffusion in the stochastic hydrodynamic model is studied and numerical simulations of uncertain traffic situations are presented.</p>

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19

Pflieger, Lance T., Clinton C. Mason, and Julio C. Facelli. "Uncertainty quantification in breast cancer risk prediction models using self-reported family health history." Journal of Clinical and Translational Science 1, no. 1 (January 20, 2017): 53–59. http://dx.doi.org/10.1017/cts.2016.9.

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Introduction. Family health history (FHx) is an important factor in breast and ovarian cancer risk assessment. As such, multiple risk prediction models rely strongly on FHx data when identifying a patient’s risk. These models were developed using verified information and when translated into a clinical setting assume that a patient’s FHx is accurate and complete. However, FHx information collected in a typical clinical setting is known to be imprecise and it is not well understood how this uncertainty may affect predictions in clinical settings. Methods. Using Monte Carlo simulations and existing measurements of uncertainty of self-reported FHx, we show how uncertainty in FHx information can alter risk classification when used in typical clinical settings. Results. We found that various models ranged from 52% to 64% for correct tier-level classification of pedigrees under a set of contrived uncertain conditions, but that significant misclassification are not negligible. Conclusions. Our work implies that (i) uncertainty quantification needs to be considered when transferring tools from a controlled research environment to a more uncertain environment (i.e, a health clinic) and (ii) better FHx collection methods are needed to reduce uncertainty in breast cancer risk prediction in clinical settings.

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20

Tang, Yongchuan, Yong Chen, and Deyun Zhou. "Measuring Uncertainty in the Negation Evidence for Multi-Source Information Fusion." Entropy 24, no. 11 (November 2, 2022): 1596. http://dx.doi.org/10.3390/e24111596.

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Dempster–Shafer evidence theory is widely used in modeling and reasoning uncertain information in real applications. Recently, a new perspective of modeling uncertain information with the negation of evidence was proposed and has attracted a lot of attention. Both the basic probability assignment (BPA) and the negation of BPA in the evidence theory framework can model and reason uncertain information. However, how to address the uncertainty in the negation information modeled as the negation of BPA is still an open issue. Inspired by the uncertainty measures in Dempster–Shafer evidence theory, a method of measuring the uncertainty in the negation evidence is proposed. The belief entropy named Deng entropy, which has attracted a lot of attention among researchers, is adopted and improved for measuring the uncertainty of negation evidence. The proposed measure is defined based on the negation function of BPA and can quantify the uncertainty of the negation evidence. In addition, an improved method of multi-source information fusion considering uncertainty quantification in the negation evidence with the new measure is proposed. Experimental results on a numerical example and a fault diagnosis problem verify the rationality and effectiveness of the proposed method in measuring and fusing uncertain information.

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21

Reichert, Peter. "Towards a comprehensive uncertainty assessment in environmental research and decision support." Water Science and Technology 81, no. 8 (January 29, 2020): 1588–96. http://dx.doi.org/10.2166/wst.2020.032.

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Abstract Uncertainty quantification is very important in environmental management to allow decision makers to consider the reliability of predictions of the consequences of decision alternatives and relate them to their risk attitudes and the uncertainty about their preferences. Nevertheless, uncertainty quantification in environmental decision support is often incomplete and the robustness of the results regarding assumptions made for uncertainty quantification is often not investigated. In this article, an attempt is made to demonstrate how uncertainty can be considered more comprehensively in environmental research and decision support by combining well-established with rarely applied statistical techniques. In particular, the following elements of uncertainty quantification are discussed: (i) using stochastic, mechanistic models that consider and propagate uncertainties from their origin to the output; (ii) profiting from the support of modern techniques of data science to increase the diversity of the exploration process, to benchmark mechanistic models, and to find new relationships; (iii) analysing structural alternatives by multi-model and non-parametric approaches; (iv) quantitatively formulating and using societal preferences in decision support; (v) explicitly considering the uncertainty of elicited preferences in addition to the uncertainty of predictions in decision support; and (vi) explicitly considering the ambiguity about prior distributions for predictions and preferences by using imprecise probabilities. In particular, (v) and (vi) have mostly been ignored in the past and a guideline is provided on how these uncertainties can be considered without significantly increasing the computational burden. The methodological approach to (v) and (vi) is based on expected expected utility theory, which extends expected utility theory to the consideration of uncertain preferences, and on imprecise, intersubjective Bayesian probabilities.

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22

Berends, Koen D., Menno W. Straatsma, Jord J. Warmink, and Suzanne J. M. H. Hulscher. "Uncertainty quantification of flood mitigation predictions and implications for interventions." Natural Hazards and Earth System Sciences 19, no. 8 (August 13, 2019): 1737–53. http://dx.doi.org/10.5194/nhess-19-1737-2019.

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Abstract. Reduction of water levels during river floods is key in preventing damage and loss of life. Computer models are used to design ways to achieve this and assist in the decision-making process. However, the predictions of computer models are inherently uncertain, and it is currently unknown to what extent that uncertainty affects predictions of the effect of flood mitigation strategies. In this study, we quantify the uncertainty of flood mitigation interventions on the Dutch River Waal, based on 39 different sources of uncertainty and 12 intervention designs. The aim of each intervention is to reduce flood water levels. Our objective is to investigate the uncertainty of model predictions of intervention effect and to explore relationships that may aid in decision-making. We identified the relative uncertainty, defined as the ratio between the confidence interval and the expected effect, as a useful metric to compare uncertainty between different interventions. Using this metric, we show that intervention effect uncertainty behaves like a traditional backwater curve with an approximately constant relative uncertainty value. In general, we observe that uncertainty scales with effect: high flood level decreases have high uncertainty, and, conversely, small effects are accompanied by small uncertainties. However, different interventions with the same expected effect do not necessarily have the same uncertainty. For example, our results show that the large-scale but relatively ineffective intervention of floodplain smoothing by removing vegetation has much higher uncertainty compared to alternative options. Finally, we show how a level of acceptable uncertainty can be defined and how this can affect the design of interventions. In general, we conclude that the uncertainty of model predictions is not large enough to invalidate model-based intervention design, nor small enough to neglect altogether. Instead, uncertainty information is valuable in the selection of alternative interventions.

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23

Poliannikov, Oleg V., and Alison E. Malcolm. "The effect of velocity uncertainty on migrated reflectors: Improvements from relative-depth imaging." GEOPHYSICS 81, no. 1 (January 1, 2016): S21—S29. http://dx.doi.org/10.1190/geo2014-0604.1.

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We have studied the problem of uncertainty quantification for migrated images. A traditional migrated image contains deterministic reconstructions of subsurface structures. However, input parameters used in migration, such as reflection data and a velocity model, are inherently uncertain. This uncertainty is carried through to the migrated images. We have used Bayesian analysis to quantify the uncertainty of the migrated structures by constructing a joint statistical distribution of the location of these structures. From this distribution, we could deduce the uncertainty in any quantity derived from these structures. We have developed the proposed framework using a simple model with velocity uncertainty in the overburden, and we estimated the absolute positions of the horizons and the relative depth of one horizon with respect to another. By quantifying the difference in the corresponding uncertainties, we found that, in this case, the relative depths of the structures could be estimated much better than their absolute depths. This analysis justifies redatuming below an uncertain overburden for the purposes of the uncertainty reduction.

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Liu, Xuejun, Hailong Tang, Xin Zhang, and Min Chen. "Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine." Applied Sciences 11, no. 18 (September 8, 2021): 8333. http://dx.doi.org/10.3390/app11188333.

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The gas turbine engine is a widely used thermodynamic system for aircraft. The demand for quantifying the uncertainty of engine performance is increasing due to the expectation of reliable engine performance design. In this paper, a fast, accurate, and robust uncertainty quantification method is proposed to investigate the impact of component performance uncertainty on the performance of a classical turboshaft engine. The Gaussian process model is firstly utilized to accurately approximate the relationships between inputs and outputs of the engine performance simulation model. Latin hypercube sampling is subsequently employed to perform uncertainty analysis of the engine performance. The accuracy, robustness, and convergence rate of the proposed method are validated by comparing with the Monte Carlo sampling method. Two main scenarios are investigated, where uncertain parameters are considered to be mutually independent and partially correlated, respectively. Finally, the variance-based sensitivity analysis is used to determine the main contributors to the engine performance uncertainty. Both approximation and sampling errors are explained in the uncertainty quantification to give more accurate results. The final results yield new insights about the engine performance uncertainty and the important component performance parameters.

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25

Han, Shuo, Molei Tao, Ufuk Topcu, Houman Owhadi, and Richard M. Murray. "Convex Optimal Uncertainty Quantification." SIAM Journal on Optimization 25, no. 3 (January 2015): 1368–87. http://dx.doi.org/10.1137/13094712x.

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Beran, Philip, Bret Stanford, and Christopher Schrock. "Uncertainty Quantification in Aeroelasticity." Annual Review of Fluid Mechanics 49, no. 1 (January 3, 2017): 361–86. http://dx.doi.org/10.1146/annurev-fluid-122414-034441.

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27

Hartmann, Matthias, and Helmut Herwartz. "DID THE INTRODUCTION OF THE EURO HAVE AN IMPACT ON INFLATION UNCERTAINTY?—AN EMPIRICAL ASSESSMENT." Macroeconomic Dynamics 18, no. 6 (May 21, 2013): 1313–25. http://dx.doi.org/10.1017/s1365100512000971.

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We compare inflation uncertainty in distinguished groups of economies. Results indicate that during the recent financial crisis the global inflation climate has become markedly more uncertain than previously. We document that in comparison to other economies, member states of the European Monetary Union are less exposed to inflation uncertainty. Three European Union members that are not part of the monetary union and five other OECD member economies serve as control groups. With regard to the quantification of inflation uncertainty, results are robust over a set of alternative estimates of the latent inflation risk processes.

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SEPAHVAND, K., S. MARBURG, and H. J. HARDTKE. "UNCERTAINTY QUANTIFICATION IN STOCHASTIC SYSTEMS USING POLYNOMIAL CHAOS EXPANSION." International Journal of Applied Mechanics 02, no. 02 (June 2010): 305–53. http://dx.doi.org/10.1142/s1758825110000524.

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In recent years, extensive research has been reported about a method which is called the generalized polynomial chaos expansion. In contrast to the sampling methods, e.g., Monte Carlo simulations, polynomial chaos expansion is a nonsampling method which represents the uncertain quantities as an expansion including the decomposition of deterministic coefficients and random orthogonal bases. The generalized polynomial chaos expansion uses more orthogonal polynomials as the expansion bases in various random spaces which are not necessarily Gaussian. A general review of uncertainty quantification methods, the theory, the construction method, and various convergence criteria of the polynomial chaos expansion are presented. We apply it to identify the uncertain parameters with predefined probability density functions. The new concepts of optimal and nonoptimal expansions are defined and it demonstrated how we can develop these expansions for random variables belonging to the various random spaces. The calculation of the polynomial coefficients for uncertain parameters by using various procedures, e.g., Galerkin projection, collocation method, and moment method is presented. A comprehensive error and accuracy analysis of the polynomial chaos method is discussed for various random variables and random processes and results are compared with the exact solution or/and Monte Carlo simulations. The method is employed for the basic stochastic differential equation and, as practical application, to solve the stochastic modal analysis of the microsensor quartz fork. We emphasize the accuracy in results and time efficiency of this nonsampling procedure for uncertainty quantification of stochastic systems in comparison with sampling techniques, e.g., Monte Carlo simulation.

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Wang, Jiajia, Hao Chen, Jing Ma, and Tong Zhang. "Research on application method of uncertainty quantification technology in equipment test identification." MATEC Web of Conferences 336 (2021): 02026. http://dx.doi.org/10.1051/matecconf/202133602026.

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This paper introduces the concepts of equipment test qualification and uncertainty quantification, and the analysis framework and process of equipment test uncertainty quantification. It analyzes the data uncertainty, model uncertainty and environmental uncertainty, and studies the corresponding uncertainty quantification theory to provide technical reference for the application of uncertainty quantification technology in the field of test identification.

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Zhao, Yingge, Lingyue Wang, Ying Li, Ruixia Jin, and Zihan Yang. "An Improved Multi-dimensional Uncertainty Quantification Method Based on DNN-DRM." Journal of Physics: Conference Series 2650, no. 1 (November 1, 2023): 012019. http://dx.doi.org/10.1088/1742-6596/2650/1/012019.

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Abstract Mathematical modeling is a method that uses mathematical methods to solve problems in real life. In the process of modeling, the inherent properties of the parameters and the change of the model design conditions will bring great uncertainty to the simulation results. In this paper, a deep neural network and dimension reduction method (DNN-DRM) is proposed to quantify the impact of parameter uncertainty on simulation results in modeling systems with multi-dimensional uncertainty, and reduce the risk caused by uncertainty. Firstly, the methods for training DNN substitute model and testing the generalization ability of models were investigated. Then the DRM based on DNN was constructed to solve the uncertain parameters in the system. In the experiments, three mathematical models with different types of complexity were modeled. Finally, the performance of the method was evaluated by probability distribution, mean and standard deviation of output values. The results show that compared with Monte Carlo simulation (MCS), the DNN-DRM can efficiently and accurately calculate the multi-dimensional uncertainty problem with a strong interaction, and effectively alleviate the “curse of dimensionality” difficulty, which provides a reference for the analysis of problems encountered in real life.

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31

Kabir, H. M. Dipu, Abbas Khosravi, Subrota K. Mondal, Mustaneer Rahman, Saeid Nahavandi, and Rajkumar Buyya. "Uncertainty-aware Decisions in Cloud Computing." ACM Computing Surveys 54, no. 4 (May 2021): 1–30. http://dx.doi.org/10.1145/3447583.

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The rapid growth of the cloud industry has increased challenges in the proper governance of the cloud infrastructure. Many intelligent systems have been developing, considering uncertainties in the cloud. Intelligent approaches with the consideration of uncertainties bring optimal management with higher profitability. Uncertainties of different levels and different types exist in various domains of cloud computing. This survey aims to discuss all types of uncertainties and their effect on different components of cloud computing. The article first presents the concept of uncertainty and its quantification. A vast number of uncertain events influence the cloud, as it is connected with the entire world through the internet. Five major uncertain parameters are identified, which are directly affected by numerous uncertain events and affect the performance of the cloud. Notable events affecting major uncertain parameters are also described. Besides, we present notable uncertainty-aware research works in cloud computing. A hype curve on uncertainty-aware approaches in the cloud is also presented to visualize current conditions and future possibilities. We expect the inauguration of numerous uncertainty-aware intelligent systems in cloud management over time. This article may provide a deeper understanding of managing cloud resources with uncertainties efficiently to future cloud researchers.

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32

Zou, Q., and M. Sester. "UNCERTAINTY REPRESENTATION AND QUANTIFICATION OF 3D BUILDING MODELS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 335–41. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-335-2022.

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Abstract. The quality of environmental perception is of great interest for localization tasks in autonomous systems. Maps, generated from the sensed information, are often used as additional spatial references in these applications. The quantification of the map uncertainties gives an insight into how reliable and complete the map is, avoiding the potential systematic deviation in pose estimation. Mapping 3D buildings in urban areas using Light detection and ranging (LiDAR) point clouds is a challenging task as it is often subject to uncertain error sources in the real world such as sensor noise and occlusions, which should be well represented in the 3D models for the downstream localization tasks. In this paper, we propose a method to model 3D building façades in complex urban scenes with uncertainty quantification, where the uncertainties of windows and façades are indicated in a probabilistic fashion. The potential locations of the missing objects (here: windows) are inferred by the available data and layout patterns with the Monte Carlo (MC) sampling approach. The proposed 3D building model and uncertainty measures are evaluated using the real-world LiDAR point clouds collected by Riegl Mobile Mapping System. The experimental results show that our uncertainty representation conveys the quality information of the estimated locations and shapes for the modelled map objects.

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Yang, Bin, Zhanran Xia, Xinyun Gao, Jing Tu, Hao Zhou, Jun Wu, and Mingzhen Li. "Research on the Application of Uncertainty Quantification (UQ) Method in High-Voltage (HV) Cable Fault Location." Energies 15, no. 22 (November 11, 2022): 8447. http://dx.doi.org/10.3390/en15228447.

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In HV cable fault location technology, line parameter uncertainty has an impact on the location criterion and affects the fault location result. Therefore, it is of great significance to study the uncertainty quantification of line parameters. In this paper, an impedance-based fault location criterion was used for an uncertainty study. Three kinds of uncertainty factors, namely the sheath resistivity per unit length, the equivalent grounding resistance on both sides, and the length of the cable section, were taken as random input variables without interaction. They were subject to random uniform distribution within a 50% amplitude variation. The relevant statistical information, such as the mean value, standard deviation and probability distribution, of the normal operation and fault state were calculated using the Monte Carlo simulation (MCS) method, the polynomial chaos expansion (PCE) method, and the univariate dimension reduction method (UDRM), respectively. Thus, the influence of uncertain factors on fault location was analyzed, and the calculation results of the three uncertainty quantification methods compared. The results indicate that: (1) UQ methods are effective for simulation analysis of fault locations, and UDRM has certain application prospects for HV fault location in practice; (2) the quantification results of the MCS, PCE, and UDRM were very close, while the mean convergence rate was significantly higher for the UDRM; (3) compared with the MCS, PCE, and UDRM, the PCE and UDRM had higher accuracy, and MCS and UDRM required less running time.

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Wells, S., A. Plotkowski, J. Coleman, M. Rolchigo, R. Carson, and M. J. M. Krane. "Uncertainty quantification for computational modelling of laser powder bed fusion." IOP Conference Series: Materials Science and Engineering 1281, no. 1 (May 1, 2023): 012024. http://dx.doi.org/10.1088/1757-899x/1281/1/012024.

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Abstract Additive manufacturing (AM) may have many advantages over traditional casting and wrought methods, but our understanding of the various processes is still limited. Computational models are useful to study and isolate underlying physics and improve our understanding of the AM process-microstructure-property relations. However, these models necessarily rely on simplifications and parameters of uncertain value. These assumptions reduce the overall reliability of the predictive capabilities of these models, so it is important to estimate the uncertainty in model output. In doing so, we quantify the effect of model limitations and identify potential areas of improvement, a procedure made possible by uncertainty quantification (UQ). Here we highlight recent work which coupled and propagated statistical and systematic uncertainties from a melt pool transport model based in OpenFOAM, through a grain scale cellular automaton code. We demonstrate how a UQ framework can identify model parameters which most significantly impact the reliability of model predictions through both models and thus provide insight for future improvements in the models and suggest measurements to reduce output uncertainty.

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Abebe, Misganaw, and Bonyong Koo. "Fatigue Life Uncertainty Quantification of Front Suspension Lower Control Arm Design." Vehicles 5, no. 3 (July 14, 2023): 859–75. http://dx.doi.org/10.3390/vehicles5030047.

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The purpose of this study is to investigate the uncertainty of the design variables of a front suspension lower control arm under fatigue-loading circumstances to estimate a reliable and robust product. This study offers a method for systematic uncertainty quantification (UQ), and the following steps were taken to achieve this: First, a finite element model was built to predict the fatigue life of the control arm under bump-loading conditions. Second, a sensitivity scheme, based on one of the global analyses, was developed to identify the model’s most and least significant design input variables. Third, physics-based and data-driven uncertainty quantification schemes were employed to quantify the model’s input parameter uncertainties via a Monte Carlo simulation. The simulations were conducted using 10,000 samples of material properties and geometrical uncertainty variables, with the coefficients of variation ranging from 1 to 3%. Finally, the confidence interval results show a deviation of about 21.74% from the mean (the baseline). As a result, by applying systematic UQ, a more reliable and robust automobile suspension control arm can be designed during the early stages of design to produce a more efficient and better approximation of fatigue life under uncertain conditions.

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Lei, Chon Lok, Sanmitra Ghosh, Dominic G. Whittaker, Yasser Aboelkassem, Kylie A. Beattie, Chris D. Cantwell, Tammo Delhaas, et al. "Considering discrepancy when calibrating a mechanistic electrophysiology model." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2173 (May 25, 2020): 20190349. http://dx.doi.org/10.1098/rsta.2019.0349.

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Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions—that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy , and we are often uncertain as to the size and consequences of this discrepancy. Here, we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes and autoregressive-moving-average models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

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Sun, Xianming, and Michèle Vanmaele. "Uncertainty Quantification of Derivative Instruments." East Asian Journal on Applied Mathematics 7, no. 2 (May 2017): 343–62. http://dx.doi.org/10.4208/eajam.100316.270117a.

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AbstractModel and parameter uncertainties are common whenever some parametric model is selected to value a derivative instrument. Combining the Monte Carlo method with the Smolyak interpolation algorithm, we propose an accurate efficient numerical procedure to quantify the uncertainty embedded in complex derivatives. Except for the value function being sufficiently smooth with respect to the model parameters, there are no requirements on the payoff or candidate models. Numerical tests carried out quantify the uncertainty of Bermudan put options and down-and-out put options under the Heston model, with each model parameter specified in an interval.

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Narayan, Akil, and Dongbin Xiu. "Distributional Sensitivity for Uncertainty Quantification." Communications in Computational Physics 10, no. 1 (July 2011): 140–60. http://dx.doi.org/10.4208/cicp.160210.300710a.

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AbstractIn this work we consider a general notion ofdistributional sensitivity, which measures the variation in solutions of a given physical/mathematical system with respect to the variation of probability distribution of the inputs. This is distinctively different from the classical sensitivity analysis, which studies the changes of solutions with respect to the values of the inputs. The general idea is measurement of sensitivity of outputs with respect to probability distributions, which is a well-studied concept in related disciplines. We adapt these ideas to present a quantitative framework in the context of uncertainty quantification for measuring such a kind of sensitivity and a set of efficient algorithms to approximate the distributional sensitivity numerically. A remarkable feature of the algorithms is that they do not incur additional computational effort in addition to a one-time stochastic solver. Therefore, an accurate stochastic computation with respect to a prior input distribution is needed only once, and the ensuing distributional sensitivity computation for different input distributions is a post-processing step. We prove that an accurate numericalmodel leads to accurate calculations of this sensitivity, which applies not just to slowly-converging Monte-Carlo estimates, but also to exponentially convergent spectral approximations. We provide computational examples to demonstrate the ease of applicability and verify the convergence claims.

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Mathieu, Sophie, Rainer von Sachs, Christian Ritter, Véronique Delouille, and Laure Lefèvre. "Uncertainty Quantification in Sunspot Counts." Astrophysical Journal 886, no. 1 (November 13, 2019): 7. http://dx.doi.org/10.3847/1538-4357/ab4990.

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40

Costa, Francisco, Andrew Clifton, Nikola Vasiljevic, and Ines Würth. "Qlunc: Quantification of lidar uncertainty." Journal of Open Source Software 6, no. 66 (October 28, 2021): 3211. http://dx.doi.org/10.21105/joss.03211.

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Gray, Genetha A., Herbert K. H. Lee, and John Guenther. "Simultaneous optimization and uncertainty quantification." Journal of Computational Methods in Sciences and Engineering 12, no. 1-2 (May 28, 2012): 99–110. http://dx.doi.org/10.3233/jcm-2012-0406.

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Pouliot, George, Emily Wisner, David Mobley, and William Hunt. "Quantification of emission factor uncertainty." Journal of the Air & Waste Management Association 62, no. 3 (January 20, 2012): 287–98. http://dx.doi.org/10.1080/10473289.2011.649155.

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43

Funfschilling, Christine, and Guillaume Perrin. "Uncertainty quantification in vehicle dynamics." Vehicle System Dynamics 57, no. 7 (April 8, 2019): 1062–86. http://dx.doi.org/10.1080/00423114.2019.1601745.

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Farmer, C. L. "Uncertainty quantification and optimal decisions." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2200 (April 2017): 20170115. http://dx.doi.org/10.1098/rspa.2017.0115.

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A mathematical model can be analysed to construct policies for action that are close to optimal for the model. If the model is accurate, such policies will be close to optimal when implemented in the real world. In this paper, the different aspects of an ideal workflow are reviewed: modelling, forecasting, evaluating forecasts, data assimilation and constructing control policies for decision-making. The example of the oil industry is used to motivate the discussion, and other examples, such as weather forecasting and precision agriculture, are used to argue that the same mathematical ideas apply in different contexts. Particular emphasis is placed on (i) uncertainty quantification in forecasting and (ii) how decisions are optimized and made robust to uncertainty in models and judgements. This necessitates full use of the relevant data and by balancing costs and benefits into the long term may suggest policies quite different from those relevant to the short term.

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Matthews, Jessica L., Elizabeth Mannshardt, and Pierre Gremaud. "Uncertainty Quantification for Climate Observations." Bulletin of the American Meteorological Society 94, no. 3 (March 2013): ES21—ES25. http://dx.doi.org/10.1175/bams-d-12-00042.1.

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Mirzayeva, A., N. A. Slavinskaya, M. Abbasi, J. H. Starcke, W. Li, and M. Frenklach. "Uncertainty Quantification in Chemical Modeling." Eurasian Chemico-Technological Journal 20, no. 1 (March 31, 2018): 33. http://dx.doi.org/10.18321/ectj706.

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A module of PrIMe automated data-centric infrastructure, Bound-to-Bound Data Collaboration (B2BDC), was used for the analysis of systematic uncertainty and data consistency of the H2/CO reaction model (73/17). In order to achieve this purpose, a dataset of 167 experimental targets (ignition delay time and laminar flame speed) and 55 active model parameters (pre-exponent factors in the Arrhenius form of the reaction rate coefficients) was constructed. Consistency analysis of experimental data from the composed dataset revealed disagreement between models and data. Two consistency measures were applied to identify the quality of experimental targets (Quantities of Interest, QoI): scalar consistency measure, which quantifies the tightening index of the constraints while still ensuring the existence of a set of the model parameter values whose associated modeling output predicts the experimental QoIs within the uncertainty bounds; and a newly-developed method of computing the vector consistency measure (VCM), which determines the minimal bound changes for QoIs initially identified as inconsistent, each bound by its own extent, while still ensuring the existence of a set of the model parameter values whose associated modeling output predicts the experimental QoIs within the uncertainty bounds. The consistency analysis suggested that elimination of 45 experimental targets, 8 of which were self- inconsistent, would lead to a consistent dataset. After that the feasible parameter set was constructed through decrease uncertainty parameters for several reaction rate coefficients. This dataset was subjected for the B2BDC framework model optimization and analysis on. Forth methods of parameter optimization were applied, including those unique in the B2BDC framework. The optimized models showed improved agreement with experimental values, as compared to the initially-assembled model. Moreover, predictions for experiments not included in the initial dataset were investigated. The results demonstrate benefits of applying the B2BDC methodology for development of predictive kinetic models.

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Ghanem, Roger G., and Steven F. Wojtkiewicz. "Special Issue on Uncertainty Quantification." SIAM Journal on Scientific Computing 26, no. 2 (January 2004): vii. http://dx.doi.org/10.1137/sjoce3000026000002000vii000001.

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Fezi, K., and M. J. M. Krane. "Uncertainty Quantification in Solidification Modelling." IOP Conference Series: Materials Science and Engineering 84 (June 11, 2015): 012001. http://dx.doi.org/10.1088/1757-899x/84/1/012001.

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Ghanem, Roger, and Xiaoping Du. "Uncertainty quantification for engineering design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, no. 2 (May 2017): 119. http://dx.doi.org/10.1017/s0890060417000026.

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Ghanem, Roger, and Xiaoping Du. "Uncertainty quantification for engineering design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, no. 3 (August 2017): 222. http://dx.doi.org/10.1017/s0890060417000129.

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