Artículos de revistas sobre el tema "Predictive uncertainty quantification"
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Cacuci, Dan Gabriel. "Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems". Energies 15, n.º 17 (1 de septiembre de 2022): 6379. http://dx.doi.org/10.3390/en15176379.
Texto completoCsillag, Daniel, Lucas Monteiro Paes, Thiago Ramos, João Vitor Romano, Rodrigo Schuller, Roberto B. Seixas, Roberto I. Oliveira y Paulo Orenstein. "AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junio de 2023): 15494–502. http://dx.doi.org/10.1609/aaai.v37i13.26837.
Texto completoLew, Jiann-Shiun y Jer-Nan Juang. "Robust Generalized Predictive Control with Uncertainty Quantification". Journal of Guidance, Control, and Dynamics 35, n.º 3 (mayo de 2012): 930–37. http://dx.doi.org/10.2514/1.54510.
Texto completoKarimi, Hamed y Reza Samavi. "Quantifying Deep Learning Model Uncertainty in Conformal Prediction". Proceedings of the AAAI Symposium Series 1, n.º 1 (3 de octubre de 2023): 142–48. http://dx.doi.org/10.1609/aaaiss.v1i1.27492.
Texto completoAkitaya, Kento y 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, n.º 3 (28 de enero de 2024): 423. http://dx.doi.org/10.3390/w16030423.
Texto completoSingh, Rishabh y Jose C. Principe. "Toward a Kernel-Based Uncertainty Decomposition Framework for Data and Models". Neural Computation 33, n.º 5 (13 de abril de 2021): 1164–98. http://dx.doi.org/10.1162/neco_a_01372.
Texto completoChen, Peng y Nicholas Zabaras. "Adaptive Locally Weighted Projection Regression Method for Uncertainty Quantification". Communications in Computational Physics 14, n.º 4 (octubre de 2013): 851–78. http://dx.doi.org/10.4208/cicp.060712.281212a.
Texto completoOmagbon, Jericho, John Doherty, Angus Yeh, Racquel Colina, John O'Sullivan, Julian McDowell, Ruanui Nicholson, Oliver J. Maclaren y Michael O'Sullivan. "Case studies of predictive uncertainty quantification for geothermal models". Geothermics 97 (diciembre de 2021): 102263. http://dx.doi.org/10.1016/j.geothermics.2021.102263.
Texto completoNitschke, C. T., P. Cinnella, D. Lucor y J. C. Chassaing. "Model-form and predictive uncertainty quantification in linear aeroelasticity". Journal of Fluids and Structures 73 (agosto de 2017): 137–61. http://dx.doi.org/10.1016/j.jfluidstructs.2017.05.007.
Texto completoMirzayeva, A., N. A. Slavinskaya, M. Abbasi, J. H. Starcke, W. Li y M. Frenklach. "Uncertainty Quantification in Chemical Modeling". Eurasian Chemico-Technological Journal 20, n.º 1 (31 de marzo de 2018): 33. http://dx.doi.org/10.18321/ectj706.
Texto completoAlbi, Giacomo, Lorenzo Pareschi y 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.
Texto completoKumar, Bhargava, Tejaswini Kumar, Swapna Nadakuditi, Hitesh Patel y Karan Gupta. "Comparing Conformal and Quantile Regression for Uncertainty Quantification: An Empirical Investigation". International Journal of Computing and Engineering 5, n.º 5 (27 de mayo de 2024): 1–8. http://dx.doi.org/10.47941/ijce.1925.
Texto completoGorle, Catherine. "Improving the predictive capability of building simulations using uncertainty quantification". Science and Technology for the Built Environment 28, n.º 5 (28 de mayo de 2022): 575–76. http://dx.doi.org/10.1080/23744731.2022.2079261.
Texto completoDelottier, Hugo, John Doherty y Philip Brunner. "Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model". Geoscientific Model Development 16, n.º 14 (26 de julio de 2023): 4213–31. http://dx.doi.org/10.5194/gmd-16-4213-2023.
Texto completoGerber, Eric A. E. y Bruce A. Craig. "A mixed effects multinomial logistic-normal model for forecasting baseball performance". Journal of Quantitative Analysis in Sports 17, n.º 3 (6 de enero de 2021): 221–39. http://dx.doi.org/10.1515/jqas-2020-0007.
Texto completoWells, S., A. Plotkowski, J. Coleman, M. Rolchigo, R. Carson y M. J. M. Krane. "Uncertainty quantification for computational modelling of laser powder bed fusion". IOP Conference Series: Materials Science and Engineering 1281, n.º 1 (1 de mayo de 2023): 012024. http://dx.doi.org/10.1088/1757-899x/1281/1/012024.
Texto completoMa, Junwei, Xiao Liu, Xiaoxu Niu, Yankun Wang, Tao Wen, Junrong Zhang y Zongxing Zou. "Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique". International Journal of Environmental Research and Public Health 17, n.º 13 (3 de julio de 2020): 4788. http://dx.doi.org/10.3390/ijerph17134788.
Texto completoFeng, Jinchao, Joshua L. Lansford, Markos A. Katsoulakis y Dionisios G. Vlachos. "Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences". Science Advances 6, n.º 42 (octubre de 2020): eabc3204. http://dx.doi.org/10.1126/sciadv.abc3204.
Texto completoBanerjee, Sourav. "Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins". Computational Biology and Bioinformatics 2, n.º 1 (2014): 7. http://dx.doi.org/10.11648/j.cbb.20140201.12.
Texto completoRiley, Matthew E. y Ramana V. Grandhi. "Quantification of model-form and predictive uncertainty for multi-physics simulation". Computers & Structures 89, n.º 11-12 (junio de 2011): 1206–13. http://dx.doi.org/10.1016/j.compstruc.2010.10.004.
Texto completoZgraggen, Jannik, Gianmarco Pizza y Lilach Goren Huber. "Uncertainty Informed Anomaly Scores with Deep Learning: Robust Fault Detection with Limited Data". PHM Society European Conference 7, n.º 1 (29 de junio de 2022): 530–40. http://dx.doi.org/10.36001/phme.2022.v7i1.3342.
Texto completoKefalas, Marios, Bas van Stein, Mitra Baratchi, Asteris Apostolidis y Thomas Baeck. "End-to-End Pipeline for Uncertainty Quantification and Remaining Useful Life Estimation: An Application on Aircraft Engines". PHM Society European Conference 7, n.º 1 (29 de junio de 2022): 245–60. http://dx.doi.org/10.36001/phme.2022.v7i1.3317.
Texto completoSætrom, Jon, Joakim Hove, Jan-Arild Skjervheim y Jon Gustav Vabø. "Improved Uncertainty Quantification in the Ensemble Kalman Filter Using Statistical Model-Selection Techniques". SPE Journal 17, n.º 01 (31 de enero de 2012): 152–62. http://dx.doi.org/10.2118/145192-pa.
Texto completoOlalusi, Oladimeji B. y Panagiotis Spyridis. "Probabilistic Studies on the Shear Strength of Slender Steel Fiber Reinforced Concrete Structures". Applied Sciences 10, n.º 19 (4 de octubre de 2020): 6955. http://dx.doi.org/10.3390/app10196955.
Texto completoDing, Jing, Yizhuang David Wang, Saqib Gulzar, Youngsoo Richard Kim y 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, n.º 4 (13 de marzo de 2020): 247–60. http://dx.doi.org/10.1177/0361198120910149.
Texto completoDogulu, N., P. López López, D. P. Solomatine, A. H. Weerts y 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, n.º 9 (10 de septiembre de 2014): 10179–233. http://dx.doi.org/10.5194/hessd-11-10179-2014.
Texto completoKarimanzira, Divas. "Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration". Knowledge 4, n.º 4 (25 de septiembre de 2024): 462–80. http://dx.doi.org/10.3390/knowledge4040025.
Texto completoCacuci, 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.
Texto completoCacuci, 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.
Texto completoSlavinskaya, 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, n.º 3 (14 de febrero de 2017): 2274–97. http://dx.doi.org/10.1021/acs.energyfuels.6b02319.
Texto completoTran, Vinh Ngoc y Jongho Kim. "Quantification of predictive uncertainty with a metamodel: toward more efficient hydrologic simulations". Stochastic Environmental Research and Risk Assessment 33, n.º 7 (julio de 2019): 1453–76. http://dx.doi.org/10.1007/s00477-019-01703-0.
Texto completoWalz, Eva-Maria, Alexander Henzi, Johanna Ziegel y Tilmann Gneiting. "Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-Valued Model Output". SIAM Review 66, n.º 1 (febrero de 2024): 91–122. http://dx.doi.org/10.1137/22m1541915.
Texto completoHeringhaus, Monika E., Yi Zhang, André Zimmermann y Lars Mikelsons. "Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference". Sensors 22, n.º 14 (20 de julio de 2022): 5408. http://dx.doi.org/10.3390/s22145408.
Texto completoIncorvaia, Gabriele, Darryl Hond y Hamid Asgari. "Uncertainty Quantification of Machine Learning Model Performance via Anomaly-Based Dataset Dissimilarity Measures". Electronics 13, n.º 5 (29 de febrero de 2024): 939. http://dx.doi.org/10.3390/electronics13050939.
Texto completoMa, Junwei, Xiaoxu Niu, Huiming Tang, Yankun Wang, Tao Wen y Junrong Zhang. "Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach". Complexity 2020 (28 de enero de 2020): 1–15. http://dx.doi.org/10.1155/2020/2624547.
Texto completoNamadchian, Ali, Mehdi Ramezani y Yuanyuan Zou. "Uncertainty quantification of model predictive control for nonlinear systems with parametric uncertainty using hybrid pseudo-spectral method". Cogent Engineering 6, n.º 1 (1 de enero de 2019): 1691803. http://dx.doi.org/10.1080/23311916.2019.1691803.
Texto completoChen, Ming, Xinhu Zhang, Kechun Shen y 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, n.º 5 (14 de mayo de 2022): 670. http://dx.doi.org/10.3390/jmse10050670.
Texto completoShrestha, Durga L., Nagendra Kayastha, Dimitri Solomatine y Roland Price. "Encapsulation of parametric uncertainty statistics by various predictive machine learning models: MLUE method". Journal of Hydroinformatics 16, n.º 1 (25 de julio de 2013): 95–113. http://dx.doi.org/10.2166/hydro.2013.242.
Texto completoYe, Yanan, Alvaro Ruiz-Martinez, Peng Wang y Daniel M. Tartakovsky. "Quantification of Predictive Uncertainty in Models of FtsZ ring assembly in Escherichia coli". Journal of Theoretical Biology 484 (enero de 2020): 110006. http://dx.doi.org/10.1016/j.jtbi.2019.110006.
Texto completoHasselman, Timothy y George Lloyd. "A top-down approach to calibration, validation, uncertainty quantification and predictive accuracy assessment". Computer Methods in Applied Mechanics and Engineering 197, n.º 29-32 (mayo de 2008): 2596–606. http://dx.doi.org/10.1016/j.cma.2007.07.031.
Texto completoXie, Shulian, Feng Xue, Weimin Zhang y Jiawei Zhu. "Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life". Machines 11, n.º 10 (25 de septiembre de 2023): 923. http://dx.doi.org/10.3390/machines11100923.
Texto completoZhu, Hong-Yu, Gang Wang, Yi Liu y Ze-Kun Zhou. "Numerical investigation of transonic buffet on supercritical airfoil considering uncertainties in wind tunnel testing". International Journal of Modern Physics B 34, n.º 14n16 (20 de abril de 2020): 2040083. http://dx.doi.org/10.1142/s0217979220400834.
Texto completoBoso, F. y D. M. Tartakovsky. "Learning on dynamic statistical manifolds". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, n.º 2239 (julio de 2020): 20200213. http://dx.doi.org/10.1098/rspa.2020.0213.
Texto completoDogulu, N., P. López López, D. P. Solomatine, A. H. Weerts y 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, n.º 7 (23 de julio de 2015): 3181–201. http://dx.doi.org/10.5194/hess-19-3181-2015.
Texto completoPandey, Deep Shankar y Qi Yu. "Evidential Conditional Neural Processes". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 8 (26 de junio de 2023): 9389–97. http://dx.doi.org/10.1609/aaai.v37i8.26125.
Texto completoDavis, Gary A. y 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, n.º 11 (16 de junio de 2019): 583–92. http://dx.doi.org/10.1177/0361198119851747.
Texto completoGupta, Ishank, Deepak Devegowda, Vikram Jayaram, Chandra Rai y Carl Sondergeld. "Machine learning regressors and their metrics to predict synthetic sonic and mechanical properties". Interpretation 7, n.º 3 (1 de agosto de 2019): SF41—SF55. http://dx.doi.org/10.1190/int-2018-0255.1.
Texto completoGuerra, Gabriel, Fernando A. Rochinha, Renato Elias, Daniel de Oliveira, Eduardo Ogasawara, Jonas Furtado Dias, Marta Mattoso y 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, n.º 1 (2012): 53–71. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.v2.i1.50.
Texto completoPeltz, James J., Dan G. Cacuci, Aurelian F. Badea y Madalina C. Badea. "Predictive Modeling Applied to a Spent Fuel Dissolver Model—II: Uncertainty Quantification and Reduction". Nuclear Science and Engineering 183, n.º 3 (1 de julio de 2016): 332–46. http://dx.doi.org/10.13182/nse15-99.
Texto completoKasiviswanathan, K. S. y K. P. Sudheer. "Quantification of the predictive uncertainty of artificial neural network based river flow forecast models". Stochastic Environmental Research and Risk Assessment 27, n.º 1 (28 de junio de 2012): 137–46. http://dx.doi.org/10.1007/s00477-012-0600-2.
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