Journal articles on the topic 'Predictive uncertainty quantification'
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Cacuci, Dan Gabriel. "Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems." Energies 15, no. 17 (September 1, 2022): 6379. http://dx.doi.org/10.3390/en15176379.
Full textCsillag, Daniel, Lucas Monteiro Paes, Thiago Ramos, João Vitor Romano, Rodrigo Schuller, Roberto B. Seixas, Roberto I. Oliveira, and Paulo Orenstein. "AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 15494–502. http://dx.doi.org/10.1609/aaai.v37i13.26837.
Full textLew, Jiann-Shiun, and Jer-Nan Juang. "Robust Generalized Predictive Control with Uncertainty Quantification." Journal of Guidance, Control, and Dynamics 35, no. 3 (May 2012): 930–37. http://dx.doi.org/10.2514/1.54510.
Full textKarimi, Hamed, and Reza Samavi. "Quantifying Deep Learning Model Uncertainty in Conformal Prediction." Proceedings of the AAAI Symposium Series 1, no. 1 (October 3, 2023): 142–48. http://dx.doi.org/10.1609/aaaiss.v1i1.27492.
Full textAkitaya, Kento, and Masaatsu Aichi. "Land Subsidence Model Inversion with the Estimation of Both Model Parameter Uncertainty and Predictive Uncertainty Using an Evolutionary-Based Data Assimilation (EDA) and Ensemble Model Output Statistics (EMOS)." Water 16, no. 3 (January 28, 2024): 423. http://dx.doi.org/10.3390/w16030423.
Full textSingh, Rishabh, and Jose C. Principe. "Toward a Kernel-Based Uncertainty Decomposition Framework for Data and Models." Neural Computation 33, no. 5 (April 13, 2021): 1164–98. http://dx.doi.org/10.1162/neco_a_01372.
Full textChen, Peng, and Nicholas Zabaras. "Adaptive Locally Weighted Projection Regression Method for Uncertainty Quantification." Communications in Computational Physics 14, no. 4 (October 2013): 851–78. http://dx.doi.org/10.4208/cicp.060712.281212a.
Full textOmagbon, Jericho, John Doherty, Angus Yeh, Racquel Colina, John O'Sullivan, Julian McDowell, Ruanui Nicholson, Oliver J. Maclaren, and Michael O'Sullivan. "Case studies of predictive uncertainty quantification for geothermal models." Geothermics 97 (December 2021): 102263. http://dx.doi.org/10.1016/j.geothermics.2021.102263.
Full textNitschke, C. T., P. Cinnella, D. Lucor, and J. C. Chassaing. "Model-form and predictive uncertainty quantification in linear aeroelasticity." Journal of Fluids and Structures 73 (August 2017): 137–61. http://dx.doi.org/10.1016/j.jfluidstructs.2017.05.007.
Full textMirzayeva, A., N. A. Slavinskaya, M. Abbasi, J. H. Starcke, W. Li, and M. Frenklach. "Uncertainty Quantification in Chemical Modeling." Eurasian Chemico-Technological Journal 20, no. 1 (March 31, 2018): 33. http://dx.doi.org/10.18321/ectj706.
Full textAlbi, Giacomo, Lorenzo Pareschi, and Mattia Zanella. "Uncertainty Quantification in Control Problems for Flocking Models." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/850124.
Full textKumar, Bhargava, Tejaswini Kumar, Swapna Nadakuditi, Hitesh Patel, and Karan Gupta. "Comparing Conformal and Quantile Regression for Uncertainty Quantification: An Empirical Investigation." International Journal of Computing and Engineering 5, no. 5 (May 27, 2024): 1–8. http://dx.doi.org/10.47941/ijce.1925.
Full textGorle, Catherine. "Improving the predictive capability of building simulations using uncertainty quantification." Science and Technology for the Built Environment 28, no. 5 (May 28, 2022): 575–76. http://dx.doi.org/10.1080/23744731.2022.2079261.
Full textDelottier, Hugo, John Doherty, and Philip Brunner. "Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model." Geoscientific Model Development 16, no. 14 (July 26, 2023): 4213–31. http://dx.doi.org/10.5194/gmd-16-4213-2023.
Full textGerber, Eric A. E., and Bruce A. Craig. "A mixed effects multinomial logistic-normal model for forecasting baseball performance." Journal of Quantitative Analysis in Sports 17, no. 3 (January 6, 2021): 221–39. http://dx.doi.org/10.1515/jqas-2020-0007.
Full textWells, S., A. Plotkowski, J. Coleman, M. Rolchigo, R. Carson, and M. J. M. Krane. "Uncertainty quantification for computational modelling of laser powder bed fusion." IOP Conference Series: Materials Science and Engineering 1281, no. 1 (May 1, 2023): 012024. http://dx.doi.org/10.1088/1757-899x/1281/1/012024.
Full textMa, Junwei, Xiao Liu, Xiaoxu Niu, Yankun Wang, Tao Wen, Junrong Zhang, and Zongxing Zou. "Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique." International Journal of Environmental Research and Public Health 17, no. 13 (July 3, 2020): 4788. http://dx.doi.org/10.3390/ijerph17134788.
Full textFeng, Jinchao, Joshua L. Lansford, Markos A. Katsoulakis, and Dionisios G. Vlachos. "Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences." Science Advances 6, no. 42 (October 2020): eabc3204. http://dx.doi.org/10.1126/sciadv.abc3204.
Full textBanerjee, Sourav. "Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins." Computational Biology and Bioinformatics 2, no. 1 (2014): 7. http://dx.doi.org/10.11648/j.cbb.20140201.12.
Full textRiley, Matthew E., and Ramana V. Grandhi. "Quantification of model-form and predictive uncertainty for multi-physics simulation." Computers & Structures 89, no. 11-12 (June 2011): 1206–13. http://dx.doi.org/10.1016/j.compstruc.2010.10.004.
Full textZgraggen, Jannik, Gianmarco Pizza, and Lilach Goren Huber. "Uncertainty Informed Anomaly Scores with Deep Learning: Robust Fault Detection with Limited Data." PHM Society European Conference 7, no. 1 (June 29, 2022): 530–40. http://dx.doi.org/10.36001/phme.2022.v7i1.3342.
Full textKefalas, Marios, Bas van Stein, Mitra Baratchi, Asteris Apostolidis, and Thomas Baeck. "End-to-End Pipeline for Uncertainty Quantification and Remaining Useful Life Estimation: An Application on Aircraft Engines." PHM Society European Conference 7, no. 1 (June 29, 2022): 245–60. http://dx.doi.org/10.36001/phme.2022.v7i1.3317.
Full textSætrom, Jon, Joakim Hove, Jan-Arild Skjervheim, and Jon Gustav Vabø. "Improved Uncertainty Quantification in the Ensemble Kalman Filter Using Statistical Model-Selection Techniques." SPE Journal 17, no. 01 (January 31, 2012): 152–62. http://dx.doi.org/10.2118/145192-pa.
Full textOlalusi, Oladimeji B., and Panagiotis Spyridis. "Probabilistic Studies on the Shear Strength of Slender Steel Fiber Reinforced Concrete Structures." Applied Sciences 10, no. 19 (October 4, 2020): 6955. http://dx.doi.org/10.3390/app10196955.
Full textDing, Jing, Yizhuang David Wang, Saqib Gulzar, Youngsoo Richard Kim, and B. Shane Underwood. "Uncertainty Quantification of Simplified Viscoelastic Continuum Damage Fatigue Model using the Bayesian Inference-Based Markov Chain Monte Carlo Method." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 4 (March 13, 2020): 247–60. http://dx.doi.org/10.1177/0361198120910149.
Full textDogulu, N., P. López López, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha. "Estimation of predictive hydrologic uncertainty using quantile regression and UNEEC methods and their comparison on contrasting catchments." Hydrology and Earth System Sciences Discussions 11, no. 9 (September 10, 2014): 10179–233. http://dx.doi.org/10.5194/hessd-11-10179-2014.
Full textKarimanzira, Divas. "Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration." Knowledge 4, no. 4 (September 25, 2024): 462–80. http://dx.doi.org/10.3390/knowledge4040025.
Full textCacuci, Dan G. "TOWARDS OVERCOMING THE CURSE OF DIMENSIONALITY IN PREDICTIVE MODELLING AND UNCERTAINTY QUANTIFICATION." EPJ Web of Conferences 247 (2021): 00002. http://dx.doi.org/10.1051/epjconf/202124700002.
Full textCacuci, Dan G. "TOWARDS OVERCOMING THE CURSE OF DIMENSIONALITY IN PREDICTIVE MODELLING AND UNCERTAINTY QUANTIFICATION." EPJ Web of Conferences 247 (2021): 20005. http://dx.doi.org/10.1051/epjconf/202124720005.
Full textSlavinskaya, N. A., M. Abbasi, J. H. Starcke, R. Whitside, A. Mirzayeva, U. Riedel, W. Li, et al. "Development of an Uncertainty Quantification Predictive Chemical Reaction Model for Syngas Combustion." Energy & Fuels 31, no. 3 (February 14, 2017): 2274–97. http://dx.doi.org/10.1021/acs.energyfuels.6b02319.
Full textTran, Vinh Ngoc, and Jongho Kim. "Quantification of predictive uncertainty with a metamodel: toward more efficient hydrologic simulations." Stochastic Environmental Research and Risk Assessment 33, no. 7 (July 2019): 1453–76. http://dx.doi.org/10.1007/s00477-019-01703-0.
Full textWalz, Eva-Maria, Alexander Henzi, Johanna Ziegel, and Tilmann Gneiting. "Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-Valued Model Output." SIAM Review 66, no. 1 (February 2024): 91–122. http://dx.doi.org/10.1137/22m1541915.
Full textHeringhaus, Monika E., Yi Zhang, André Zimmermann, and Lars Mikelsons. "Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference." Sensors 22, no. 14 (July 20, 2022): 5408. http://dx.doi.org/10.3390/s22145408.
Full textIncorvaia, Gabriele, Darryl Hond, and Hamid Asgari. "Uncertainty Quantification of Machine Learning Model Performance via Anomaly-Based Dataset Dissimilarity Measures." Electronics 13, no. 5 (February 29, 2024): 939. http://dx.doi.org/10.3390/electronics13050939.
Full textMa, Junwei, Xiaoxu Niu, Huiming Tang, Yankun Wang, Tao Wen, and Junrong Zhang. "Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach." Complexity 2020 (January 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/2624547.
Full textNamadchian, Ali, Mehdi Ramezani, and Yuanyuan Zou. "Uncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method." Cogent Engineering 6, no. 1 (January 1, 2019): 1691803. http://dx.doi.org/10.1080/23311916.2019.1691803.
Full textChen, Ming, Xinhu Zhang, Kechun Shen, and Guang Pan. "Sparse Polynomial Chaos Expansion for Uncertainty Quantification of Composite Cylindrical Shell with Geometrical and Material Uncertainty." Journal of Marine Science and Engineering 10, no. 5 (May 14, 2022): 670. http://dx.doi.org/10.3390/jmse10050670.
Full textShrestha, Durga L., Nagendra Kayastha, Dimitri Solomatine, and Roland Price. "Encapsulation of parametric uncertainty statistics by various predictive machine learning models: MLUE method." Journal of Hydroinformatics 16, no. 1 (July 25, 2013): 95–113. http://dx.doi.org/10.2166/hydro.2013.242.
Full textYe, Yanan, Alvaro Ruiz-Martinez, Peng Wang, and Daniel M. Tartakovsky. "Quantification of Predictive Uncertainty in Models of FtsZ ring assembly in Escherichia coli." Journal of Theoretical Biology 484 (January 2020): 110006. http://dx.doi.org/10.1016/j.jtbi.2019.110006.
Full textHasselman, Timothy, and George Lloyd. "A top-down approach to calibration, validation, uncertainty quantification and predictive accuracy assessment." Computer Methods in Applied Mechanics and Engineering 197, no. 29-32 (May 2008): 2596–606. http://dx.doi.org/10.1016/j.cma.2007.07.031.
Full textXie, Shulian, Feng Xue, Weimin Zhang, and Jiawei Zhu. "Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life." Machines 11, no. 10 (September 25, 2023): 923. http://dx.doi.org/10.3390/machines11100923.
Full textZhu, Hong-Yu, Gang Wang, Yi Liu, and Ze-Kun Zhou. "Numerical investigation of transonic buffet on supercritical airfoil considering uncertainties in wind tunnel testing." International Journal of Modern Physics B 34, no. 14n16 (April 20, 2020): 2040083. http://dx.doi.org/10.1142/s0217979220400834.
Full textBoso, F., and D. M. Tartakovsky. "Learning on dynamic statistical manifolds." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2239 (July 2020): 20200213. http://dx.doi.org/10.1098/rspa.2020.0213.
Full textDogulu, N., P. López López, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha. "Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments." Hydrology and Earth System Sciences 19, no. 7 (July 23, 2015): 3181–201. http://dx.doi.org/10.5194/hess-19-3181-2015.
Full textPandey, Deep Shankar, and Qi Yu. "Evidential Conditional Neural Processes." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (June 26, 2023): 9389–97. http://dx.doi.org/10.1609/aaai.v37i8.26125.
Full textDavis, Gary A., and Christopher Cheong. "Pedestrian Injury Severity vs. Vehicle Impact Speed: Uncertainty Quantification and Calibration to Local Conditions." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (June 16, 2019): 583–92. http://dx.doi.org/10.1177/0361198119851747.
Full textGupta, Ishank, Deepak Devegowda, Vikram Jayaram, Chandra Rai, and Carl Sondergeld. "Machine learning regressors and their metrics to predict synthetic sonic and mechanical properties." Interpretation 7, no. 3 (August 1, 2019): SF41—SF55. http://dx.doi.org/10.1190/int-2018-0255.1.
Full textGuerra, Gabriel, Fernando A. Rochinha, Renato Elias, Daniel de Oliveira, Eduardo Ogasawara, Jonas Furtado Dias, Marta Mattoso, and Alvaro L. G. A. Coutinho. "UNCERTAINTY QUANTIFICATION IN COMPUTATIONAL PREDICTIVE MODELS FOR FLUID DYNAMICS USING A WORKFLOW MANAGEMENT ENGINE." International Journal for Uncertainty Quantification 2, no. 1 (2012): 53–71. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.v2.i1.50.
Full textPeltz, James J., Dan G. Cacuci, Aurelian F. Badea, and Madalina C. Badea. "Predictive Modeling Applied to a Spent Fuel Dissolver Model—II: Uncertainty Quantification and Reduction." Nuclear Science and Engineering 183, no. 3 (July 1, 2016): 332–46. http://dx.doi.org/10.13182/nse15-99.
Full textKasiviswanathan, K. S., and K. P. Sudheer. "Quantification of the predictive uncertainty of artificial neural network based river flow forecast models." Stochastic Environmental Research and Risk Assessment 27, no. 1 (June 28, 2012): 137–46. http://dx.doi.org/10.1007/s00477-012-0600-2.
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