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Littérature scientifique sur le sujet « Uncertainly quantification »

Auteur : Grafiati

Publié le 14 décembre 2024

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Uncertainly quantification"

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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Thèses sur le sujet "Uncertainly quantification"

Nguyen, Trieu Nhat Thanh. "Modélisation et simulation d'éléments finis du système pelvien humain vers un outil d'aide à la décision fiable : incertitude des données et des lois de comportement." Electronic Thesis or Diss., Centrale Lille Institut, 2024. http://www.theses.fr/2024CLIL0015.

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Cette thèse a développé une approche originale pour quantifier les incertitudes liées aux propriétés hyperélastiques des tissus mous, en utilisant à la fois des probabilités précises et imprécises. Le protocole de calcul a été étendu pour quantifier les incertitudes dans les contractions utérines actives lors des simulations du deuxième stade du travail. De plus, une simulation de la descente foetale a été créée, intégrant des données de contraction utérine active basées sur l'IRM et une quantification d'incertitude associée. L'étude a révélé que l'Expansion du Chaos Polynomial (PCE) non intru

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Elfverson, Daniel. "Multiscale Methods and Uncertainty Quantification." Doctoral thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-262354.

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In this thesis we consider two great challenges in computer simulations of partial differential equations: multiscale data, varying over multiple scales in space and time, and data uncertainty, due to lack of or inexact measurements. We develop a multiscale method based on a coarse scale correction, using localized fine scale computations. We prove that the error in the solution produced by the multiscale method decays independently of the fine scale variation in the data or the computational domain. We consider the following aspects of multiscale methods: continuous and discontinuous underlyi

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Parkinson, Matthew. "Uncertainty quantification in Radiative Transport." Thesis, University of Bath, 2019. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767610.

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We study how uncertainty in the input data of the Radiative Transport equation (RTE), affects the distribution of (functionals of) its solution (the output data). The RTE is an integro-differential equation, in up to seven independent variables, that models the behaviour of rarefied particles (such as photons and neutrons) in a domain. Its applications include nuclear reactor design, radiation shielding, medical imaging, optical tomography and astrophysics. We focus on the RTE in the context of nuclear reactor physics where, to design and maintain safe reactors, understanding the effects of un

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Carson, J. "Uncertainty quantification in palaeoclimate reconstruction." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/29076/.

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Studying the dynamics of the palaeoclimate is a challenging problem. Part of the challenge lies in the fact that our understanding must be based on only a single realisation of the climate system. With only one climate history, it is essential that palaeoclimate data are used to their full extent, and that uncertainties arising from both data and modelling are well characterised. This is the motivation behind this thesis, which explores approaches for uncertainty quantification in problems related to palaeoclimate reconstruction. We focus on uncertainty quantification problems for the glacial-

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Boopathy, Komahan. "Uncertainty Quantification and Optimization Under Uncertainty Using Surrogate Models." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398302731.

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Kalmikov, Alexander G. "Uncertainty Quantification in ocean state estimation." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/79291.

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Thesis (Ph. D.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 158-160).<br>Quantifying uncertainty and error bounds is a key outstanding challenge in ocean state estimation and climate research. It is particularly difficult due to the large dimensionality of this nonlinear estimation problem and the number of uncertain variables involved. The "Estimating the Circul

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Malenova, Gabriela. "Uncertainty quantification for high frequency waves." Licentiate thesis, KTH, Numerisk analys, NA, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186287.

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We consider high frequency waves satisfying the scalar wave equationwith highly oscillatory initial data. The speed of propagation of the mediumas well as the phase and amplitude of the initial data is assumed to beuncertain, described by a finite number of independent random variables withknown probability distributions. We introduce quantities of interest (QoIs)aslocal averages of the squared modulus of the wave solution, or itsderivatives.The regularity of these QoIs in terms of the input random parameters and thewavelength is important for uncertainty quantification methods based oninterpo

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Roy, Pamphile. "Uncertainty quantification in high dimensional problems." Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0038.

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Les incertitudes font partie du monde qui nous entoure. Se limiter à une seule valeur nominale est bien souvent trop restrictif, et ce d'autant plus lorsqu'il est question de systèmes complexes. Comprendre la nature et l'impact de ces incertitudes est devenu un aspect important de tout travail d'ingénierie. D'un point de vue sociétal, les incertitudes jouent un rôle important dans les processus de décision. Les dernières recommandations de la Commission européenne en matière d'analyses des risques souligne l'importance du traitement des incertitudes. Afin de comprendre les incertitudes, une no

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Alvarado, Martin Guillermo. "Quantification of uncertainty during history matching." Texas A&M University, 2003. http://hdl.handle.net/1969/463.

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Jimenez, Edwin. "Uncertainty quantification of nonlinear stochastic phenomena." Tallahassee, Florida : Florida State University, 2009. http://etd.lib.fsu.edu/theses/available/etd-11092009-161351/.

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Thesis (Ph. D.)--Florida State University, 2009.<br>Advisor: M.Y. Hussaini, Florida State University, College of Arts and Sciences, Dept. of Mathematics. Title and description from dissertation home page (viewed on Mar. 16, 2010). Document formatted into pages; contains xii, 113 pages. Includes bibliographical references.

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Livres sur le sujet "Uncertainly quantification"

Soize, Christian. Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0.

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Sullivan, T. J. Introduction to Uncertainty Quantification. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23395-6.

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Ghanem, Roger, David Higdon, and Houman Owhadi, eds. Handbook of Uncertainty Quantification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-11259-6.

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Souza de Cursi, Eduardo. Uncertainty Quantification using R. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17785-9.

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Souza de Cursi, Eduardo. Uncertainty Quantification with R. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-48208-3.

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Le Maître, O. P., and Omar M. Knio. Spectral Methods for Uncertainty Quantification. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3520-2.

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Dienstfrey, Andrew M., and Ronald F. Boisvert, eds. Uncertainty Quantification in Scientific Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32677-6.

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McClarren, Ryan G. Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0.

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Bijl, Hester, Didier Lucor, Siddhartha Mishra, and Christoph Schwab, eds. Uncertainty Quantification in Computational Fluid Dynamics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00885-1.

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Bardsley, Johnathan M. Computational Uncertainty Quantification for Inverse Problems. Society for Industrial and Applied Mathematics, 2018. http://dx.doi.org/10.1137/1.9781611975383.

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Chapitres de livres sur le sujet "Uncertainly quantification"

Soize, Christian. "Fundamental Notions in Stochastic Modeling of Uncertainties and Their Propagation in Computational Models." In Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_1.

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Soize, Christian. "Random Fields and Uncertainty Quantification in Solid Mechanics of Continuum Media." In Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_10.

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Soize, Christian. "Elements of Probability Theory." In Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_2.

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Soize, Christian. "Markov Process and Stochastic Differential Equation." In Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_3.

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Soize, Christian. "MCMC Methods for Generating Realizations and for Estimating the Mathematical Expectation of Nonlinear Mappings of Random Vectors." In Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_4.

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Soize, Christian. "Fundamental Probabilistic Tools for Stochastic Modeling of Uncertainties." In Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_5.

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Soize, Christian. "Brief Overview of Stochastic Solvers for the Propagation of Uncertainties." In Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_6.

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Soize, Christian. "Fundamental Tools for Statistical Inverse Problems." In Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_7.

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Soize, Christian. "Uncertainty Quantification in Computational Structural Dynamics and Vibroacoustics." In Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_8.

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Soize, Christian. "Robust Analysis with Respect to the Uncertainties for Analysis, Updating, Optimization, and Design." In Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_9.

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Actes de conférences sur le sujet "Uncertainly quantification"

Misaka, Takashi, Shigeru Obayashi, and Shinkyu Jeong. "Uncertainly Quantification of Lidar-Derived Wake Vortex Parameters with/without Data Assimilation (Invited)." In 8th AIAA Atmospheric and Space Environments Conference. American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-3271.

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Zhang, Qian, Shenren Xu, Xianjun Yu, Jiaxin Liu, Dingxi Wang, and Xiuquan Huang. "Quantification of Compressor Aerodynamic Performance Deviation due to Manufacturing Uncertainty Using the Adjoint Method." In GPPS Xi'an21. GPPS, 2022. http://dx.doi.org/10.33737/gpps21-tc-59.

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Efficient and accurate Uncertainly Quantification (UQ) is essential for robust design optimization and manufacturing tolerance control to limit performance deterioration and scattering of compressor blades. Built upon past work, the current study investigates the applicability of the newly proposed adjoint method based nonlinear model for accurate and efficient Uncertainly Quantification (UQ) of performance deviations of a transonic compressor blade due to geometric variability. To demonstrate the advantages of the adjoint method based nonlinear model, two other methods, namely, the adjoint me

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Lee, Nian-Ze, Yen-Shi Wang, and Jie-Hong R. Jiang. "Solving Stochastic Boolean Satisfiability under Random-Exist Quantification." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/96.

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Stochastic Boolean Satisfiability (SSAT) is a powerful formalism to represent computational problems with uncertainly, such as belief network inference and propositional probabilistic planning. Solving SSAT formulas lies in the same complexity class (PSPACE-complete) as solving Quantified Boolean Formula (QBF). While many endeavors have been made to enhance QBF solving, SSAT has drawn relatively less attention in recent years. This paper focuses on random-exist quantified SSAT formulas, and proposes an algorithm combining binary decision diagram (BDD), logic synthesis, and modern SAT technique

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Kotteda, V. M. Krushnarao, Anitha Kommu, Vinod Kumar, and William Spotz. "Uncertainty Quantification of a Fluidized Bed Reactor." In ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ajkfluids2019-4844.

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Abstract Fluidized beds are used in a wide range of applications in gasification, combustion, and process engineering. Multiphase flow in such applications involves numerous uncertain parameters. Uncertainty quantification provides uncertainty in syngas yield and efficiency of coal/biomass gasification in a power plant. Techniques such as sensitivity analysis are useful in identifying parameters that have the most influence on the quantities of interest. Also, it helps to decrease the computational cost of the uncertainty quantification and optimize the reactor. We carried out a nondeterminist

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Jayaraman, Buvana, Manas Khurana, Andrew Wissink, and Rohit Jain. "Uncertainty Quantification Approach for Rotorcraft Simulations." In Vertical Flight Society 78th Annual Forum & Technology Display. The Vertical Flight Society, 2022. http://dx.doi.org/10.4050/f-0078-2022-17462.

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The goal of this work is to quantify the uncertainty and sensitivity of freestream velocity and wind direction on wing download and rotor thrust predictions for the Joint Vertical Experiment (JVX) tiltrotor configuration in hover. Even light winds can have a significant impact on hover performance. Accordingly, an accurate representation of hover performance with uncertainties due to variability in atmospheric wind conditions needs to be understood. To support this effort, mid-fidelity simulations with a Reduced Order Aerodynamic Model in CREATETM-AV Helios is used to generate training and tes

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Budzien, Joanne, James Byerly, Rob Aulwes, Rao Garimella, Angela Herring, and Jon Woodring. "Linking Material Models Between Codes: Establishing Thermodynamic Consistency." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86808.

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Abstract One increasingly important workflow for multiphysics simulations is linking simulation codes that have different physics models and different regimes for which they have been optimized. The science question for this scoping work was evaluating the compatibility of physics models on both sides of a link to ensure a smooth simulation continuation was possible. The VVUQ aspects were establishing the most important physics aspects for a credible simulation. The most important aspect was determined to be thermodynamic consistency such that nothing unphysical would be encountered during the

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Eça, L., K. Dowding, and P. J. Roache. "On the Application of the Area Metric to Validation Exercises of Stochastic Simulations." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86809.

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Abstract This paper discusses the application of the Area Metric to the quantification of modeling errors. The focus of the discussion is the effect of the shape of the two distributions on the result produced by the Area Metric. Two different examples that assume negligible experimental and numerical errors are presented: the first case has experimental and simulated quantities of interest defined by normal distributions that require the definition of a mean value and a standard deviation; the second example is taken from the V&V10.1 ASME Standard that applies the Area Metric to quantify

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Davis, Brad, Gregory Langone, and Nicholas Reisweber. "Sensitivity Analysis and Bayesian Calibration of a Holmquist-Johnson-Cook Material Model for Cellular Concrete Subjected to Impact Loading." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86800.

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Abstract Periodic updates to small caliber weapon systems and projectiles used in military and law enforcement have resulted in consistently increasing material penetration capabilities. With each new generation, ballistics technology outpaces the lifecycle replacement of live-fire training facilities. For this reason, it is necessary to develop and maintain constitutive material models for use in analyzing the effects new threats will have on existing facilities and for designing new training facilities using numerical methods. This project utilizes material testing data to characterize cellu

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"VVUQ2022 Front Matter." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-fm1.

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Kirsch, Jared, Nima Fathi, and Joshua Hubbard. "Validation Analysis of Medium-Scale Methanol Pool Fire." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86806.

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Abstract A 30-cm diameter methanol pool fire was modeled using Sandia National Laboratories SIERRA/Fuego turbulent reacting flow code. Large Eddy Simulation (LES) with subgrid turbulent kinetic energy closure was used as the turbulence model. Combustion was modeled and simulated using a strained laminar flamelet library approach. Radiative heat transfer was modeled using the gray-gas approximation. In this investigation, the area validation metric (AVM) is employed to examine simulation results against experimental data. Time-averaged values of temperature and axial velocity at multiple locati

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Rapports d'organisations sur le sujet "Uncertainly quantification"

Caldeira, Joao. Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1623354.

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Urban, Nathan Mark. Climate Uncertainty Quantification at LANL. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1250690.

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Thiagarajan, J. Uncertainty Quantification in Scientific ML. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1670557.

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Stracuzzi, David, Maximillian Chen, Michael Darling, Matthew Peterson, and Charlie Vollmer. Uncertainty Quantification for Machine Learning. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1733262.

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Karpius, Peter. Nuclide Identification, Quantification, and Uncertainty. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1782632.

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Croft, Stephen, and Andrew Nicholson. OR14-V-Uncertainty-PD2La Uncertainty Quantification Workshop Report. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1784220.

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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes

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Seifried, Jeffrey E. Adjoint-Based Uncertainty Quantification with MCNP. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1110395.

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Srinivasan, Gowri. Need for Uncertainty Quantification in Predictions. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1191117.

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De Bord, Sarah. Tutorial examples for uncertainty quantification methods. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1213490.

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