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1

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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Vallam, P., X. S. Qin, and J. J. Yu. "Uncertainty Quantification of Hydrologic Model." APCBEE Procedia 10 (2014): 219–23. http://dx.doi.org/10.1016/j.apcbee.2014.10.042.

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3

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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4

Franck, Isabell M., and P. S. Koutsourelakis. "Constitutive model error and uncertainty quantification." PAMM 17, no. 1 (2017): 865–68. http://dx.doi.org/10.1002/pamm.201710400.

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5

de Vries, Douwe K., and Paul M. J. Den Van Hof. "Quantification of model uncertainty from data." International Journal of Robust and Nonlinear Control 4, no. 2 (1994): 301–19. http://dx.doi.org/10.1002/rnc.4590040206.

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6

Kamga, P. H. T., B. Li, M. McKerns, et al. "Optimal uncertainty quantification with model uncertainty and legacy data." Journal of the Mechanics and Physics of Solids 72 (December 2014): 1–19. http://dx.doi.org/10.1016/j.jmps.2014.07.007.

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7

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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8

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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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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10

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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11

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 (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
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12

Chung, Gunhui, Kyu Bum Sim, and Eung Seok Kim. "Uncertainty Quantification Index of SWMM Model Parameters." Journal of the Korean Water Resources Association 48, no. 2 (2015): 105–14. http://dx.doi.org/10.3741/jkwra.2015.48.2.105.

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YAMAZAKI, Wataru. "Uncertainty Quantification via Variable Fidelity Kriging Model." JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 60, no. 2 (2012): 80–88. http://dx.doi.org/10.2322/jjsass.60.80.

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14

Ye, Ming, Philip D. Meyer, Yu-Feng Lin, and Shlomo P. Neuman. "Quantification of model uncertainty in environmental modeling." Stochastic Environmental Research and Risk Assessment 24, no. 6 (2010): 807–8. http://dx.doi.org/10.1007/s00477-010-0377-0.

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15

Adhikari, S. "On the quantification of damping model uncertainty." Journal of Sound and Vibration 306, no. 1-2 (2007): 153–71. http://dx.doi.org/10.1016/j.jsv.2007.05.022.

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16

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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17

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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18

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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19

Mueller, Michael E., and Venkat Raman. "Model form uncertainty quantification in turbulent combustion simulations: Peer models." Combustion and Flame 187 (January 2018): 137–46. http://dx.doi.org/10.1016/j.combustflame.2017.09.011.

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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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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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Wang, Lujia, Hyunsoo Yoon, Andrea Hawkins-Daarud, et al. "NIMG-52. UNCERTAINTY QUANTIFICATION IN RADIOMICS." Neuro-Oncology 21, Supplement_6 (2019): vi172—vi173. http://dx.doi.org/10.1093/neuonc/noz175.721.

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Abstract INTRODUCTION The quantification of intratumoral heterogeneity – through radiomics-based approaches - can help resolve the regionally distinct genetic drug targets that may co-exist within a single Glioblastoma (GBM) tumor. While this offers potential diagnostic value under the paradigm of individualized oncology, clinical decision-making must also consider the degree of uncertainty associated with each model. In this study, we evaluate the performance of a novel machine-learning (ML) algorithm, called Gaussian Process (GP) modeling, that can quantify the impact of multiple sources of
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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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Zimoń, Małgorzata, Robert Sawko, David Emerson, and Christopher Thompson. "Uncertainty Quantification at the Molecular–Continuum Model Interface." Fluids 2, no. 1 (2017): 12. http://dx.doi.org/10.3390/fluids2010012.

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Naozuka, Gustavo Taiji, Emanuelle Arantes Paixão, João Vitor Oliveira Silva, Maurício Pessoa da Cunha Menezes, and Regina Cerqueira Almeida. "Model Comparison and Uncertainty Quantification in Tumor Growth." Trends in Computational and Applied Mathematics 22, no. 3 (2021): 495–514. http://dx.doi.org/10.5540/tcam.2021.022.03.00495.

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Mathematical and computational modeling have been increasingly applied in many areas of cancer research, aiming to improve the understanding of tumorigenic mechanisms and to suggest more effective therapy protocols. The mathematical description of the tumor growth dynamics is often made using the exponential, logistic, and Gompertz models. However, recent literature has suggested that the Allee effect may play an important role in the early stages of tumor dynamics, including cancer relapse and metastasis. For a model to provide reliable predictions, it is necessary to have a rigorous evaluati
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Lew, Jiann-Shiun, Lee H. Keel, and Jer-Nan Juang. "Quantification of parametric uncertainty via an interval model." Journal of Guidance, Control, and Dynamics 17, no. 6 (1994): 1212–18. http://dx.doi.org/10.2514/3.21335.

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Wang, Hai, and David A. Sheen. "Combustion kinetic model uncertainty quantification, propagation and minimization." Progress in Energy and Combustion Science 47 (April 2015): 1–31. http://dx.doi.org/10.1016/j.pecs.2014.10.002.

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28

Park, Inseok, Hemanth K. Amarchinta, and Ramana V. Grandhi. "A Bayesian approach for quantification of model uncertainty." Reliability Engineering & System Safety 95, no. 7 (2010): 777–85. http://dx.doi.org/10.1016/j.ress.2010.02.015.

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Li, Jing, and Panos Stinis. "Mesh refinement for uncertainty quantification through model reduction." Journal of Computational Physics 280 (January 2015): 164–83. http://dx.doi.org/10.1016/j.jcp.2014.09.021.

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Shen, Maohao, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, and Gregory Wornell. "Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9772–81. http://dx.doi.org/10.1609/aaai.v37i8.26167.

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It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures. Existing methods mainly resolve this issue by retraining the entire model to impose the uncertainty quantification capability so that the learned model can achieve desired performance in accuracy and uncertainty prediction simultaneously. However, training the model from scratch is computationally expensive, and a trade-off might exist between prediction accuracy and uncertainty quantification. To this end, we consider a more practical po
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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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Karimi, Hamed, and Reza Samavi. "Quantifying Deep Learning Model Uncertainty in Conformal Prediction." Proceedings of the AAAI Symposium Series 1, no. 1 (2023): 142–48. http://dx.doi.org/10.1609/aaaiss.v1i1.27492.

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Precise estimation of predictive uncertainty in deep neural networks is a critical requirement for reliable decision-making in machine learning and statistical modeling, particularly in the context of medical AI. Conformal Prediction (CP) has emerged as a promising framework for representing the model uncertainty by providing well-calibrated confidence levels for individual predictions. However, the quantification of model uncertainty in conformal prediction remains an active research area, yet to be fully addressed. In this paper, we explore state-of-the-art CP methodologies and their theoret
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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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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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Tang, Hesheng, Dawei Li, Lixin Deng, and Songtao Xue. "Evidential uncertainty quantification of the Park–Ang damage model in performance based design." Engineering Computations 35, no. 7 (2018): 2480–501. http://dx.doi.org/10.1108/ec-11-2017-0466.

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Purpose This paper aims to develop a comprehensive uncertainty quantification method using evidence theory for Park–Ang damage index-based performance design in which epistemic uncertainties are considered. Various sources of uncertainty emanating from the database of the cyclic test results of RC members provided by the Pacific Earthquake Engineering Research Center are taken into account. Design/methodology/approach In this paper, an uncertainty quantification methodology based on evidence theory is presented for the whole process of performance-based seismic design (PBSD), while considering
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Gerstoft, Peter, and Ishan D. Khurjekar. "Uncertainty quantification for acoustical problems." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A213. http://dx.doi.org/10.1121/10.0027342.

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Acoustical parameter estimation is a routine task in many domains and is typically done using signal processing methods. The performance of existing estimation methods is affected due to external uncertainty and yet the methods provide no measure of confidence in the outputs. Hence it is crucial to quantify uncertainty in the estimates before real-world deployment. Conformal prediction is a simple method to obtain statistically valid prediction intervals from an estimation model. In this work, conformal prediction is used for obtaining statistically valid uncertainty intervals for various acou
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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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Tian, Yudong, Grey S. Nearing, Christa D. Peters-Lidard, Kenneth W. Harrison, and Ling Tang. "Performance Metrics, Error Modeling, and Uncertainty Quantification." Monthly Weather Review 144, no. 2 (2016): 607–13. http://dx.doi.org/10.1175/mwr-d-15-0087.1.

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Abstract A common set of statistical metrics has been used to summarize the performance of models or measurements—the most widely used ones being bias, mean square error, and linear correlation coefficient. They assume linear, additive, Gaussian errors, and they are interdependent, incomplete, and incapable of directly quantifying uncertainty. The authors demonstrate that these metrics can be directly derived from the parameters of the simple linear error model. Since a correct error model captures the full error information, it is argued that the specification of a parametric error model shou
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Marepally, Koushik, Yong Su Jung, James Baeder, and Ganesh Vijayakumar. "Uncertainty quantification of wind turbine airfoil aerodynamics with geometric uncertainty." Journal of Physics: Conference Series 2265, no. 4 (2022): 042041. http://dx.doi.org/10.1088/1742-6596/2265/4/042041.

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Abstract An artificial neural network based reduced order model (ROM) is developed to predict the load coefficients and performance of wind turbine airfoils. The model is trained using a representative database of 972 wind turbine airfoil shapes generated by perturbing the design parameters in each of 12 baseline airfoils defining commercially relevant modern wind turbines. The predictions from our ROM show excellent agreement with the CFD data, with a 99th percentile maximum errors of 0.03 in lift-coefficient, 2 in lift-to-drag ratio and 0.002 in pitching moment coefficient. A Monte-Carlo bas
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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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Mannina, Giorgio, and Gaspare Viviani. "An urban drainage stormwater quality model: Model development and uncertainty quantification." Journal of Hydrology 381, no. 3-4 (2010): 248–65. http://dx.doi.org/10.1016/j.jhydrol.2009.11.047.

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Pirot, Guillaume, Ranee Joshi, Jérémie Giraud, Mark Douglas Lindsay, and Mark Walter Jessell. "loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification." Geoscientific Model Development 15, no. 12 (2022): 4689–708. http://dx.doi.org/10.5194/gmd-15-4689-2022.

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Abstract. To support the needs of practitioners regarding 3D geological modelling and uncertainty quantification in the field, in particular from the mining industry, we propose a Python package called loopUI-0.1 that provides a set of local and global indicators to measure uncertainty and features dissimilarities among an ensemble of voxet models. Results are presented of a survey launched among practitioners in the mineral industry, enquiring about their modelling and uncertainty quantification practice and needs. It reveals that practitioners acknowledge the importance of uncertainty quanti
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Kaplan, David. "On the Quantification of Model Uncertainty: A Bayesian Perspective." Psychometrika 86, no. 1 (2021): 215–38. http://dx.doi.org/10.1007/s11336-021-09754-5.

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Jung, Yeong-Ki, Kyoungsik Chang, and Jae Hyun Bae. "Uncertainty Quantification of GEKO Model Coefficients on Compressible Flows." International Journal of Aerospace Engineering 2021 (June 7, 2021): 1–17. http://dx.doi.org/10.1155/2021/9998449.

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In the present work, supersonic flows over an axisymmetric base and a 24-deg compression ramp are investigated using the generalized k - ω (GEKO) model implemented in the commercial software, ANSYS FLUENT. GEKO is a two-equation model based on the k - ω formulation, and some specified model coefficients can be tuned depending on the flow characteristics. Uncertainty quantification (UQ) analysis is incorporated to quantify the uncertainty of the model coefficients and to calibrate the coefficients. The Latin hypercube sampling (LHS) method is used for sampling independent input parameters as a
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Kampouris, Konstantinos, Vassilios Vervatis, John Karagiorgos, and Sarantis Sofianos. "Oil spill model uncertainty quantification using an atmospheric ensemble." Ocean Science 17, no. 4 (2021): 919–34. http://dx.doi.org/10.5194/os-17-919-2021.

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Abstract. We investigate the impact of atmospheric forcing uncertainties on the prediction of the dispersion of pollutants in the marine environment. Ensemble simulations consisting of 50 members were carried out using the ECMWF ensemble prediction system and the oil spill model MEDSLIK-II in the Aegean Sea. A deterministic control run using the unperturbed wind of the ECMWF high-resolution system served as reference for the oil spill prediction. We considered the oil spill rates and duration to be similar to major accidents of the past (e.g., the Prestige case) and we performed simulations fo
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46

Osypov, Konstantin, Yi Yang, Aimé Fournier, et al. "Model-uncertainty quantification in seismic tomography: method and applications." Geophysical Prospecting 61, no. 6 (2013): 1114–34. http://dx.doi.org/10.1111/1365-2478.12058.

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47

Islam, Tanvir, Prashant K. Srivastava, and George P. Petropoulos. "Uncertainty Quantification in the Infrared Surface Emissivity Model (ISEM)." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 12 (2016): 5888–92. http://dx.doi.org/10.1109/jstars.2016.2557303.

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48

Liu, D. S., and H. G. Matthies. "Uncertainty quantification with spectral approximations of a flood model." IOP Conference Series: Materials Science and Engineering 10 (June 1, 2010): 012208. http://dx.doi.org/10.1088/1757-899x/10/1/012208.

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49

Li, Mingyang, and Zequn Wang. "Surrogate model uncertainty quantification for reliability-based design optimization." Reliability Engineering & System Safety 192 (December 2019): 106432. http://dx.doi.org/10.1016/j.ress.2019.03.039.

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50

Hariri-Ardebili, M. A., and V. E. Saouma. "Sensitivity and uncertainty quantification of the cohesive crack model." Engineering Fracture Mechanics 155 (April 2016): 18–35. http://dx.doi.org/10.1016/j.engfracmech.2016.01.008.

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