Inscrição na biblioteca: Guest
International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING

Volume 4, Edição 1, 2014, pp. 63-93
DOI: 10.1615/Int.J.UncertaintyQuantification.2013006821
Get accessDownload

RESUMO

Uncertainty quantification in complex physical models is often challenged by the computational expense of these models. One often needs to operate under the assumption of sparsely available model simulations. This issue is even more critical when models include a large number of input parameters. This "curse of dimensionality", in particular, leads to a prohibitively large number of basis terms in spectral methods for uncertainty quantification, such as polynomial chaos (PC) methods. In this work, we implement a PC-based surrogate model construction that "learns" and retains only the most relevant basis terms of the PC expansion, using sparse Bayesian learning. This dramatically reduces the dimensionality of the problem, making it more amenable to further analysis such as sensitivity or calibration studies. The model of interest is the community land model with about 80 input parameters, which also exhibits nonsmooth input-output behavior. We enhanced the methodology by a clustering and classifying procedure that leads to a piecewise-PC surrogate thereby dealing with nonlinearity. We then obtain global sensitivity information for five outputs with respect to all input parameters using less than 10,000 model simulations−a very small number for an 80-dimensional input parameter space.

CITADO POR
  1. Rogers Alistair, The use and misuse of V c,max in Earth System Models, Photosynthesis Research, 119, 1-2, 2014. Crossref

  2. Hou Thomas Y., Liu Pengfei, A heterogeneous stochastic FEM framework for elliptic PDEs, Journal of Computational Physics, 281, 2015. Crossref

  3. Hampton Jerrad, Doostan Alireza, Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies, Journal of Computational Physics, 280, 2015. Crossref

  4. Ray J., Hou Z., Huang M., Sargsyan K., Swiler L., Bayesian Calibration of the Community Land Model Using Surrogates, SIAM/ASA Journal on Uncertainty Quantification, 3, 1, 2015. Crossref

  5. Ray Jaideep, Lefantzi Sophia, Arunajatesan Srinivasan, DeChant Lawrence J., Bayesian Calibration of a RANS Model with a Complex Response Surface - A Case Study with Jet-in-Crossflow Configuration, 45th AIAA Fluid Dynamics Conference, 2015. Crossref

  6. Yang Xiu, Lei Huan, Baker Nathan A., Lin Guang, Enhancing sparsity of Hermite polynomial expansions by iterative rotations, Journal of Computational Physics, 307, 2016. Crossref

  7. Jakeman J.D., Eldred M.S., Sargsyan K., Enhancing ℓ1-minimization estimates of polynomial chaos expansions using basis selection, Journal of Computational Physics, 289, 2015. Crossref

  8. Pau George Shu Heng, Shen Chaopeng, Riley William J., Liu Yaning, Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models, Water Resources Research, 52, 2, 2016. Crossref

  9. Guo Wei, Lin Guang, Christlieb Andrew, Qiu Jingmei, An Adaptive WENO Collocation Method for Differential Equations with Random Coefficients, Mathematics, 4, 2, 2016. Crossref

  10. Huang Maoyi, Ray Jaideep, Hou Zhangshuan, Ren Huiying, Liu Ying, Swiler Laura, On the applicability of surrogate-based Markov chain Monte Carlo-Bayesian inversion to the Community Land Model: Case studies at flux tower sites, Journal of Geophysical Research: Atmospheres, 121, 13, 2016. Crossref

  11. Ray Jaideep, Lefantzi Sophia, Arunajatesan Srinivasan, Dechant Lawrence, Bayesian Parameter Estimation of a k-ε Model for Accurate Jet-in-Crossflow Simulations, AIAA Journal, 54, 8, 2016. Crossref

  12. Sargsyan Khachik, Surrogate Models for Uncertainty Propagation and Sensitivity Analysis, in Handbook of Uncertainty Quantification, 2015. Crossref

  13. Debusschere Bert, Sargsyan Khachik, Safta Cosmin, Chowdhary Kenny, Uncertainty Quantification Toolkit (UQTk), in Handbook of Uncertainty Quantification, 2015. Crossref

  14. Nagel Joseph B., Sudret Bruno, Spectral likelihood expansions for Bayesian inference, Journal of Computational Physics, 309, 2016. Crossref

  15. Le Gratiet Loïc, Marelli Stefano, Sudret Bruno, Metamodel-Based Sensitivity Analysis: Polynomial Chaos Expansions and Gaussian Processes, in Handbook of Uncertainty Quantification, 2017. Crossref

  16. Rogers Alistair, Serbin Shawn P., Ely Kim S., Sloan Victoria L., Wullschleger Stan D., Terrestrial biosphere models underestimate photosynthetic capacity and CO 2 assimilation in the Arctic , New Phytologist, 216, 4, 2017. Crossref

  17. Schöbi Roland, Sudret Bruno, Uncertainty propagation of p-boxes using sparse polynomial chaos expansions, Journal of Computational Physics, 339, 2017. Crossref

  18. Walker Anthony P., Quaife Tristan, Bodegom Peter M., De Kauwe Martin G., Keenan Trevor F., Joiner Joanna, Lomas Mark R., MacBean Natasha, Xu Chongang, Yang Xiaojuan, Woodward F. Ian, The impact of alternative trait‐scaling hypotheses for the maximum photosynthetic carboxylation rate ( V cmax ) on global gross primary production , New Phytologist, 215, 4, 2017. Crossref

  19. Liu Yaning, Pau George Shu Heng, Finsterle Stefan, Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data, Computers & Geosciences, 108, 2017. Crossref

  20. Salehi Saeed, Raisee Mehrdad, Cervantes Michel J., Nourbakhsh Ahmad, Efficient uncertainty quantification of stochastic CFD problems using sparse polynomial chaos and compressed sensing, Computers & Fluids, 154, 2017. Crossref

  21. Lu Dan, Ricciuto Daniel, Walker Anthony, Safta Cosmin, Munger William, Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods, Biogeosciences, 14, 18, 2017. Crossref

  22. Iversen Colleen M., McCormack M. Luke, Powell A. Shafer, Blackwood Christopher B., Freschet Grégoire T., Kattge Jens, Roumet Catherine, Stover Daniel B., Soudzilovskaia Nadejda A., Valverde‐Barrantes Oscar J., Bodegom Peter M., Violle Cyrille, A global Fine‐Root Ecology Database to address below‐ground challenges in plant ecology, New Phytologist, 215, 1, 2017. Crossref

  23. Wang Mu, Lin Guang, Pothen Alex, Using automatic differentiation for compressive sensing in uncertainty quantification, Optimization Methods and Software, 33, 4-6, 2018. Crossref

  24. Huan Xun, Safta Cosmin, Sargsyan Khachik, Geraci Gianluca, Eldred Michael S., Vane Zachary, Lacaze Guilhem, Oefelein Joseph C., Najm Habib N., Global Sensitivity Analysis and Quantification of Model Error for Large Eddy Simulation in Scramjet Design, 19th AIAA Non-Deterministic Approaches Conference, 2017. Crossref

  25. Shao Qian, Younes Anis, Fahs Marwan, Mara Thierry A., Bayesian sparse polynomial chaos expansion for global sensitivity analysis, Computer Methods in Applied Mechanics and Engineering, 318, 2017. Crossref

  26. Mao J., Ricciuto D. M., Thornton P. E., Warren J. M., King A. W., Shi X., Iversen C. M., Norby R. J., Evaluating the Community Land Model in a pine stand with shading manipulations and <sup>13</sup>CO<sub>2</sub> labeling, Biogeosciences, 13, 3, 2016. Crossref

  27. Hampton Jerrad, Doostan Alireza, Compressive Sampling Methods for Sparse Polynomial Chaos Expansions, in Handbook of Uncertainty Quantification, 2017. Crossref

  28. Sargsyan Khachik, Surrogate Models for Uncertainty Propagation and Sensitivity Analysis, in Handbook of Uncertainty Quantification, 2017. Crossref

  29. Debusschere Bert, Sargsyan Khachik, Safta Cosmin, Chowdhary Kenny, Uncertainty Quantification Toolkit (UQTk), in Handbook of Uncertainty Quantification, 2017. Crossref

  30. Alemazkoor Negin, Meidani Hadi, Divide and conquer: An incremental sparsity promoting compressive sampling approach for polynomial chaos expansions, Computer Methods in Applied Mechanics and Engineering, 318, 2017. Crossref

  31. Chen Ming, Griffis Tim J., Baker John M., Wood Jeffrey D., Meyers Tilden, Suyker Andrew, Comparing crop growth and carbon budgets simulated across AmeriFlux agricultural sites using the Community Land Model (CLM), Agricultural and Forest Meteorology, 256-257, 2018. Crossref

  32. Li Jianduo, Chen Fei, Zhang Guo, Barlage Michael, Gan Yanjun, Xin Yufei, Wang Chen, Impacts of Land Cover and Soil Texture Uncertainty on Land Model Simulations Over the Central Tibetan Plateau, Journal of Advances in Modeling Earth Systems, 10, 9, 2018. Crossref

  33. Karagiannis Georgios, Konomi Bledar A., Lin Guang, A Bayesian mixed shrinkage prior procedure for spatial–stochastic basis selection and evaluation of gPC expansions: Applications to elliptic SPDEs, Journal of Computational Physics, 284, 2015. Crossref

  34. Diaz Paul, Doostan Alireza, Hampton Jerrad, Sparse polynomial chaos expansions via compressed sensing and D-optimal design, Computer Methods in Applied Mechanics and Engineering, 336, 2018. Crossref

  35. Salehi Saeed, Raisee Mehrdad, Cervantes Michel J., Nourbakhsh Ahmad, An efficient multifidelity ℓ1-minimization method for sparse polynomial chaos, Computer Methods in Applied Mechanics and Engineering, 334, 2018. Crossref

  36. Kenny Joseph P., Sargsyan Khachik, Knight Samuel, Michelogiannakis George, Wilke Jeremiah J., The Pitfalls of Provisioning Exascale Networks: A Trace Replay Analysis for Understanding Communication Performance, in High Performance Computing, 10876, 2018. Crossref

  37. Tsilifis P., Ghanem R. G., Bayesian adaptation of chaos representations using variational inference and sampling on geodesics, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 474, 2217, 2018. Crossref

  38. Ricciuto Daniel, Sargsyan Khachik, Thornton Peter, The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model, Journal of Advances in Modeling Earth Systems, 10, 2, 2018. Crossref

  39. Massoud Elias C., Emulation of environmental models using polynomial chaos expansion, Environmental Modelling & Software, 111, 2019. Crossref

  40. Farcaş Ionuţ-Gabriel, Sârbu Paul Cristian, Bungartz Hans-Joachim, Neckel Tobias, Uekermann Benjamin, Multilevel Adaptive Stochastic Collocation with Dimensionality Reduction, in Sparse Grids and Applications - Miami 2016, 123, 2018. Crossref

  41. Raczka Brett, Dietze Michael C., Serbin Shawn P., Davis Kenneth J., What Limits Predictive Certainty of Long‐Term Carbon Uptake?, Journal of Geophysical Research: Biogeosciences, 123, 12, 2018. Crossref

  42. Huan Xun, Safta Cosmin, Sargsyan Khachik, Vane Zachary P., Lacaze Guilhem, Oefelein Joseph C., Najm Habib N., Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions, SIAM/ASA Journal on Uncertainty Quantification, 6, 2, 2018. Crossref

  43. Safta C., Ricciuto D. M., Sargsyan K., Debusschere B., Najm H. N., Williams M., Thornton P. E., Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model, Geoscientific Model Development, 8, 7, 2015. Crossref

  44. Huan Xun, Safta Cosmin, Sargsyan Khachik, Geraci Gianluca, Eldred Michael S., Vane Zachary P., Lacaze Guilhem, Oefelein Joseph C., Najm Habib N., Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations, AIAA Journal, 56, 3, 2018. Crossref

  45. Sun Xin, Tu Qingrui, Chen Jinfu, Zhang Chengwen, Duan Xianzhong, Probabilistic load flow calculation based on sparse polynomial chaos expansion, IET Generation, Transmission & Distribution, 12, 11, 2018. Crossref

  46. Hampton Jerrad, Doostan Alireza, Basis adaptive sample efficient polynomial chaos (BASE-PC), Journal of Computational Physics, 371, 2018. Crossref

  47. Yang Xiu, Li Weixuan, Tartakovsky Alexandre, Sliced-Inverse-Regression--Aided Rotated Compressive Sensing Method for Uncertainty Quantification, SIAM/ASA Journal on Uncertainty Quantification, 6, 4, 2018. Crossref

  48. Hampton Jerrad, Doostan Alireza, Compressive Sampling Methods for Sparse Polynomial Chaos Expansions, in Handbook of Uncertainty Quantification, 2015. Crossref

  49. Papaioannou Iason, Ehre Max, Straub Daniel, PLS-based adaptation for efficient PCE representation in high dimensions, Journal of Computational Physics, 387, 2019. Crossref

  50. Rai Prashant, Sargsyan Khachik, Najm Habib, Hirata So, Sparse low rank approximation of potential energy surfaces with applications in estimation of anharmonic zero point energies and frequencies, Journal of Mathematical Chemistry, 57, 7, 2019. Crossref

  51. Lu Dan, Ricciuto Daniel, Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques, Geoscientific Model Development, 12, 5, 2019. Crossref

  52. Massoud Elias C., Xu Chonggang, Fisher Rosie A., Knox Ryan G., Walker Anthony P., Serbin Shawn P., Christoffersen Bradley O., Holm Jennifer A., Kueppers Lara M., Ricciuto Daniel M., Wei Liang, Johnson Daniel J., Chambers Jeffrey Q., Koven Charlie D., McDowell Nate G., Vrugt Jasper A., Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES), Geoscientific Model Development, 12, 9, 2019. Crossref

  53. Rogers Alistair, Serbin Shawn P., Ely Kim S., Wullschleger Stan D., Terrestrial biosphere models may overestimate Arctic CO 2 assimilation if they do not account for decreased quantum yield and convexity at low temperature , New Phytologist, 223, 1, 2019. Crossref

  54. Nagel Joseph B., Rieckermann Jörg, Sudret Bruno, Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation, Reliability Engineering & System Safety, 195, 2020. Crossref

  55. Tsilifis Panagiotis, Huan Xun, Safta Cosmin, Sargsyan Khachik, Lacaze Guilhem, Oefelein Joseph C., Najm Habib N., Ghanem Roger G., Compressive sensing adaptation for polynomial chaos expansions, Journal of Computational Physics, 380, 2019. Crossref

  56. Gratiet Loïc Le, Marelli Stefano, Sudret Bruno, Metamodel-Based Sensitivity Analysis: Polynomial Chaos Expansions and Gaussian Processes, in Handbook of Uncertainty Quantification, 2015. Crossref

  57. Fisher R. A., Muszala S., Verteinstein M., Lawrence P., Xu C., McDowell N. G., Knox R. G., Koven C., Holm J., Rogers B. M., Spessa A., Lawrence D., Bonan G., Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED), Geoscientific Model Development, 8, 11, 2015. Crossref

  58. Adelmann Andreas, On Nonintrusive Uncertainty Quantification and Surrogate Model Construction in Particle Accelerator Modeling, SIAM/ASA Journal on Uncertainty Quantification, 7, 2, 2019. Crossref

  59. Dwelle M. Chase, Kim Jongho, Sargsyan Khachik, Ivanov Valeriy Y., Streamflow, stomata, and soil pits: Sources of inference for complex models with fast, robust uncertainty quantification, Advances in Water Resources, 125, 2019. Crossref

  60. ADCOCK BEN, HUYBRECHS DAAN, APPROXIMATING SMOOTH, MULTIVARIATE FUNCTIONS ON IRREGULAR DOMAINS, Forum of Mathematics, Sigma, 8, 2020. Crossref

  61. Griffiths Natalie A., Hanson Paul J., Ricciuto Daniel M., Iversen Colleen M., Jensen Anna M., Malhotra Avni, McFarlane Karis J., Norby Richard J., Sargsyan Khachik, Sebestyen Stephen D., Shi Xiaoying, Walker Anthony P., Ward Eric J., Warren Jeffrey M., Weston David J., Temporal and Spatial Variation in Peatland Carbon Cycling and Implications for Interpreting Responses of an Ecosystem-Scale Warming Experiment, Soil Science Society of America Journal, 81, 6, 2017. Crossref

  62. Fisher Rosie A., Koven Charles D., Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems, Journal of Advances in Modeling Earth Systems, 12, 4, 2020. Crossref

  63. Tsilifis Panagiotis, Papaioannou Iason, Straub Daniel, Nobile Fabio, Sparse Polynomial Chaos expansions using variational relevance vector machines, Journal of Computational Physics, 416, 2020. Crossref

  64. Tran Vinh Ngoc, Dwelle M. Chase, Sargsyan Khachik, Ivanov Valeriy Y., Kim Jongho, A Novel Modeling Framework for Computationally Efficient and Accurate Real‐Time Ensemble Flood Forecasting With Uncertainty Quantification, Water Resources Research, 56, 3, 2020. Crossref

  65. Kougioumtzoglou Ioannis A., Petromichelakis Ioannis, Psaros Apostolos F., Sparse representations and compressive sampling approaches in engineering mechanics: A review of theoretical concepts and diverse applications, Probabilistic Engineering Mechanics, 61, 2020. Crossref

  66. Lu Han, Shen Qiuyang, Chen Jiefu, Wu Xuqing, Fu Xin, Khalil Mohammad, Safta Cosmin, Huang Yueqin, Bifidelity Gradient-Based Approach for Nonlinear Well-Logging Inverse Problems, IEEE Journal on Multiscale and Multiphysics Computational Techniques, 5, 2020. Crossref

  67. Pepper Nick, Montomoli Francesco, Sharma Sanjiv, Data fusion for Uncertainty Quantification with Non-Intrusive Polynomial Chaos, Computer Methods in Applied Mechanics and Engineering, 374, 2021. Crossref

  68. Shi Xiaoying, Ricciuto Daniel M., Thornton Peter E., Xu Xiaofeng, Yuan Fengming, Norby Richard J., Walker Anthony P., Warren Jeffrey M., Mao Jiafu, Hanson Paul J., Meng Lin, Weston David, Griffiths Natalie A., Extending a land-surface model with <i>Sphagnum</i> moss to simulate responses of a northern temperate bog to whole ecosystem warming and elevated CO<sub>2</sub>, Biogeosciences, 18, 2, 2021. Crossref

  69. Hu Zhangli, Mansour Rami, Olsson Mårten, Du Xiaoping, Second-order reliability methods: a review and comparative study, Structural and Multidisciplinary Optimization, 64, 6, 2021. Crossref

  70. Zhang Jian, Yue Xinxin, Qiu Jiajia, Zhuo Lijun, Zhu Jianguo, Sparse polynomial chaos expansion based on Bregman-iterative greedy coordinate descent for global sensitivity analysis, Mechanical Systems and Signal Processing, 157, 2021. Crossref

  71. Tran Vinh Ngoc, Kim Jongho, A robust surrogate data assimilation approach to real-time forecasting using polynomial chaos expansion, Journal of Hydrology, 598, 2021. Crossref

  72. Wang Jingyu, Fan Jiwen, Feng Zhe, Zhang Kai, Roesler Erika, Hillman Benjamin, Shpund Jacob, Lin Wuyin, Xie Shaocheng, Impact of a New Cloud Microphysics Parameterization on the Simulations of Mesoscale Convective Systems in E3SM, Journal of Advances in Modeling Earth Systems, 13, 11, 2021. Crossref

  73. Tran Vinh Ngoc, Kim Jongho, Toward an Efficient Uncertainty Quantification of Streamflow Predictions Using Sparse Polynomial Chaos Expansion, Water, 13, 2, 2021. Crossref

  74. Bhattacharyya Biswarup, Structural reliability analysis by a Bayesian sparse polynomial chaos expansion, Structural Safety, 90, 2021. Crossref

  75. Frey Matthias, Adelmann Andreas, Global sensitivity analysis on numerical solver parameters of Particle-In-Cell models in particle accelerator systems, Computer Physics Communications, 258, 2021. Crossref

  76. Ely Kim S., Rogers Alistair, Agarwal Deborah A., Ainsworth Elizabeth A., Albert Loren P., Ali Ashehad, Anderson Jeremiah, Aspinwall Michael J., Bellasio Chandra, Bernacchi Carl, Bonnage Steve, Buckley Thomas N., Bunce James, Burnett Angela C., Busch Florian A., Cavanagh Amanda, Cernusak Lucas A., Crystal-Ornelas Robert, Damerow Joan, Davidson Kenneth J., De Kauwe Martin G., Dietze Michael C., Domingues Tomas F., Dusenge Mirindi Eric, Ellsworth David S., Evans John R., Gauthier Paul P.G., Gimenez Bruno O., Gordon Elizabeth P., Gough Christopher M., Halbritter Aud H., Hanson David T., Heskel Mary, Hogan J. Aaron, Hupp Jason R., Jardine Kolby, Kattge Jens, Keenan Trevor, Kromdijk Johannes, Kumarathunge Dushan P., Lamour Julien, Leakey Andrew D.B., LeBauer David S., Li Qianyu, Lundgren Marjorie R., McDowell Nate, Meacham-Hensold Katherine, Medlyn Belinda E., Moore David J.P., Negrón-Juárez Robinson, Niinemets Ülo, Osborne Colin P., Pivovaroff Alexandria L., Poorter Hendrik, Reed Sasha C., Ryu Youngryel, Sanz-Saez Alvaro, Schmiege Stephanie C., Serbin Shawn P., Sharkey Thomas D., Slot Martijn, Smith Nicholas G., Sonawane Balasaheb V., South Paul F., Souza Daisy C., Stinziano Joseph Ronald, Stuart-Haëntjens Ellen, Taylor Samuel H., Tejera Mauricio D., Uddling Johan, Vandvik Vigdis, Varadharajan Charuleka, Walker Anthony P., Walker Berkley J., Warren Jeffrey M., Way Danielle A., Wolfe Brett T., Wu Jin, Wullschleger Stan D., Xu Chonggang, Yan Zhengbing, Yang Dedi, A reporting format for leaf-level gas exchange data and metadata, Ecological Informatics, 61, 2021. Crossref

  77. Nasution Muhammad Ridlo Erdata, Palar Pramudita S., Hadi Bambang K., Widagdo Djarot, Zuhal Lavi, Yudhanto Arief, Uncertainty Quantification and Sensitivity Analysis for In-plane Thermo-mechanical Properties of 3-D Textile Composites, AIAA SCITECH 2022 Forum, 2022. Crossref

  78. Lu Han, Chen Jiefu, Wu Xuqing, Fu Xin, Khalil Mohammad, Safta Cosmin, Huang Yueqin, Efficient Bi-fidelity Gradient-Based Method for Non-Linear Inverse Problems, 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020. Crossref

  79. Ivanov Valeriy Y., Xu Donghui, Dwelle M. Chase, Sargsyan Khachik, Wright Daniel B., Katopodes Nikolaos, Kim Jongho, Tran Vinh Ngoc, Warnock April, Fatichi Simone, Burlando Paolo, Caporali Enrica, Restrepo Pedro, Sanders Brett F., Chaney Molly M., Nunes Ana M. B., Nardi Fernando, Vivoni Enrique R., Istanbulluoglu Erkan, Bisht Gautam, Bras Rafael L., Breaking Down the Computational Barriers to Real‐Time Urban Flood Forecasting, Geophysical Research Letters, 48, 20, 2021. Crossref

  80. Karagiannis Georgios, Hou Zhangshuan, Huang Maoyi, Lin Guang, Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework, Computation, 10, 5, 2022. Crossref

  81. Tran Vinh Ngoc, Kim Jongho, Robust and efficient uncertainty quantification for extreme events that deviate significantly from the training dataset using polynomial chaos-kriging, Journal of Hydrology, 609, 2022. Crossref

  82. Ricciuto Daniel M., Xu Xiaofeng, Shi Xiaoying, Wang Yihui, Song Xia, Schadt Christopher W., Griffiths Natalie A., Mao Jiafu, Warren Jeffrey M., Thornton Peter E., Chanton Jeff, Keller Jason K., Bridgham Scott D., Gutknecht Jessica, Sebestyen Stephen D., Finzi Adrien, Kolka Randall, Hanson Paul J., An Integrative Model for Soil Biogeochemistry and Methane Processes: I. Model Structure and Sensitivity Analysis, Journal of Geophysical Research: Biogeosciences, 126, 8, 2021. Crossref

  83. Lüthen Nora, Marelli Stefano, Sudret Bruno, Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark, SIAM/ASA Journal on Uncertainty Quantification, 9, 2, 2021. Crossref

  84. Meng Lin, Mao Jiafu, Ricciuto Daniel M., Shi Xiaoying, Richardson Andrew D., Hanson Paul J, Warren Jeffrey M., Zhou Yuyu, Li Xuecao, Zhang Li, Schädel Christina, Evaluation and modification of ELM seasonal deciduous phenology against observations in a southern boreal peatland forest, Agricultural and Forest Meteorology, 308-309, 2021. Crossref

  85. He Yue, Peng Shushi, Liu Yongwen, Li Xiangyi, Wang Kai, Ciais Philippe, Arain M. Altaf, Fang Yuanyuan, Fisher Joshua B., Goll Daniel, Hayes Daniel, Huntzinger Deborah N., Ito Akihiko, Jain Atul K., Janssens Ivan A., Mao Jiafu, Matteo Campioli, Michalak Anna M., Peng Changhui, Peñuelas Josep, Poulter Benjamin, Qin Dahe, Ricciuto Daniel M., Schaefer Kevin, Schwalm Christopher R., Shi Xiaoying, Tian Hanqin, Vicca Sara, Wei Yaxing, Zeng Ning, Zhu Qiuan, Global vegetation biomass production efficiency constrained by models and observations, Global Change Biology, 26, 3, 2020. Crossref

  86. Rogers Alistair, Serbin Shawn P., Way Danielle A., Reducing model uncertainty of climate change impacts on high latitude carbon assimilation, Global Change Biology, 28, 4, 2022. Crossref

  87. Ehre Max, Papaioannou Iason, Sudret Bruno, Straub Daniel, Sequential Active Learning of Low-Dimensional Model Representations for Reliability Analysis, SIAM Journal on Scientific Computing, 44, 3, 2022. Crossref

  88. Zeng Sijie, Duan Xiaojun, Chen Jiangtao, Yan Liang, Optimized sparse polynomial chaos expansion with entropy regularization, Advances in Aerodynamics, 4, 1, 2022. Crossref

  89. Xu Donghui, Bisht Gautam, Sargsyan Khachik, Liao Chang, Leung L. Ruby, Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1, Geoscientific Model Development, 15, 12, 2022. Crossref

Portal Digital Begell Biblioteca digital da Begell eBooks Diários Referências e Anais Coleções de pesquisa Políticas de preços e assinaturas Begell House Contato Language English 中文 Русский Português German French Spain