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Journal articles on the topic 'Uncertainly quantification'

Author: Grafiati

Published: 14 December 2024

Last updated: 31 July 2025

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1

Jalaian, Brian, Michael Lee, and Stephen Russell. "Uncertain Context: Uncertainty Quantification in Machine Learning." AI Magazine 40, no. 4 (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 uncertaint

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Cacuci, Dan Gabriel. "Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems." Energies 15, no. 17 (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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Verdonck, H., O. Hach, J. D. Polman, et al. "-An open-source framework for the uncertainty quantification of aeroelastic wind turbine simulation tools." Journal of Physics: Conference Series 2265, no. 4 (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 sensitiv

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Massoli, Pierpaolo. "A Robust Approach to Uncertainty Quantification in Deep Learning." Current Research in Statistics & Mathematics 4, no. 1 (2025): 01–10. https://doi.org/10.33140/crsm.04.01.01.

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This study proposes a novel approach for quantifying the uncertainty of a deep learning model by investigating the coverage as well as the adaptivity of its prediction intervals in a Conformal Prediction context. The model investigated is designed to impute the equivalent household income by taking both specific household group characteristics and relevant features of the main income gainer into account as it is known that there are well-known correlations in literature. The imputation of such variable is critical as outliers occur or the required information for computing it is not entirely a

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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 (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 betwee

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

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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 (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 i

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Tuczyński, Tomasz, and Jerzy Stopa. "Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm." Energies 16, no. 3 (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 p

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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 (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 (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 suf

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

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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 (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 us

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

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Xu, Ting. "Uncertainty, Ignorance and Decision-Making." Amicus Curiae 3, no. 1 (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 arg

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Umavezi, Joshua Uzezi. "Bayesian Deep Learning for Uncertainty Quantification in Financial Stress Testing and Risk Forecasting." International Journal of Research Publication and Reviews 6, no. 5 (2025): 6540–55. https://doi.org/10.55248/gengpi.6.0525.1786.

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Bin, Junchi, Ran Zhang, Rui Wang, et al. "An Efficient and Uncertainty-Aware Decision Support System for Disaster Response Using Aerial Imagery." Sensors 22, no. 19 (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 effi

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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 different

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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 (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 exist

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Tang, Yongchuan, Yong Chen, and Deyun Zhou. "Measuring Uncertainty in the Negation Evidence for Multi-Source Information Fusion." Entropy 24, no. 11 (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,

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23

Reichert, Peter. "Towards a comprehensive uncertainty assessment in environmental research and decision support." Water Science and Technology 81, no. 8 (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

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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 (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 interventio

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Poliannikov, Oleg V., and Alison E. Malcolm. "The effect of velocity uncertainty on migrated reflectors: Improvements from relative-depth imaging." GEOPHYSICS 81, no. 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 derive

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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 (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

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Han, Shuo, Molei Tao, Ufuk Topcu, Houman Owhadi, and Richard M. Murray. "Convex Optimal Uncertainty Quantification." SIAM Journal on Optimization 25, no. 3 (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 (2017): 361–86. http://dx.doi.org/10.1146/annurev-fluid-122414-034441.

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Gayathri, G. Roopa. "Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47103.

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Abstract: This study benchmarks probabilistic deep learning methods for license plate recognition (LPR), focusing on enhancing accuracy and reliability under real-world conditions. Utilizing a dataset of license plate images, the approach includes comprehensive preprocessing steps such as resizing, normalization, augmentation, and super-resolution to handle low-quality inputs. The dataset is split into training, validation, and testing subsets, with the test set emphasizing out-of-distribution (OOD) scenarios. The system employs convolutional neural networks (CNNs), probabilistic models like S

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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 (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

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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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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 (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

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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 (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 test

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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 (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 qua

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MA Jie, 马杰, 王晓冰 WANG Xiaobing, 牛青林 NIU Qinglin та 董士奎 DONG Shikui. "复燃反应速率对尾焰红外辐射不确定度量化分析". Infrared and Laser Engineering 53, № 10 (2024): 20240301. https://doi.org/10.3788/irla20240301.

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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 noi

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Yang, Bin, Zhanran Xia, Xinyun Gao, et al. "Research on the Application of Uncertainty Quantification (UQ) Method in High-Voltage (HV) Cable Fault Location." Energies 15, no. 22 (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 unifo

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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 (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 qu

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Abebe, Misganaw, and Bonyong Koo. "Fatigue Life Uncertainty Quantification of Front Suspension Lower Control Arm Design." Vehicles 5, no. 3 (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 variabl

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Galal, Osama Hussein, and Eman Alruwaili. "Uncertainty Quantification of Herschel–Bulkley Fluids in Rectangular Ducts Due to Stochastic Parameters and Boundary Conditions." Axioms 14, no. 7 (2025): 492. https://doi.org/10.3390/axioms14070492.

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This study presents an innovative approach to quantifying uncertainty in Herschel–Bulkley (H-B) fluid flow through rectangular ducts, analyzing four scenarios: uncertain apparent viscosity (Case I), uncertain pressure gradient (Case II), uncertain boundary conditions (Case III) and uncertain apparent viscosity and pressure gradient (Case IV). Using the stochastic finite difference with homogeneous chaos (SFDHC) method, we produce probability density functions (PDFs) of fluid velocity with exceptional computational efficiency (243 times faster), matching the accuracy of Monte Carlo simulation (

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Most, Thomas. "Inverse Uncertainty Quantification in Material Parameter Calibration Using Probabilistic and Interval Approaches." Applied Mechanics 6, no. 1 (2025): 14. https://doi.org/10.3390/applmech6010014.

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In model calibration, the identification of the unknown parameter values themselves, but also the uncertainty of these model parameters, due to uncertain measurements or model outputs might be required. The analysis of parameter uncertainty helps us understand the calibration problem better. Investigations on the parameter sensitivity and the uniqueness of the identified parameters could be addressed within uncertainty quantification. In this paper, we investigate different probabilistic approaches for this purpose, which identify the unknown parameters as multivariate distribution functions.

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Lei, Chon Lok, Sanmitra Ghosh, Dominic G. Whittaker, 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 (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 real

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Sun, Xianming, and Michèle Vanmaele. "Uncertainty Quantification of Derivative Instruments." East Asian Journal on Applied Mathematics 7, no. 2 (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 mod

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Narayan, Akil, and Dongbin Xiu. "Distributional Sensitivity for Uncertainty Quantification." Communications in Computational Physics 10, no. 1 (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 framewo

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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 (2019): 7. http://dx.doi.org/10.3847/1538-4357/ab4990.

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Costa, Francisco, Andrew Clifton, Nikola Vasiljevic, and Ines Würth. "Qlunc: Quantification of lidar uncertainty." Journal of Open Source Software 6, no. 66 (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 (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 (2012): 287–98. http://dx.doi.org/10.1080/10473289.2011.649155.

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Funfschilling, Christine, and Guillaume Perrin. "Uncertainty quantification in vehicle dynamics." Vehicle System Dynamics 57, no. 7 (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 (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 app

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