A-Z索引
Xiu, Dongbin
Professor and Ohio Eminent Scholar
dxiu@purdue.edu; dxiu@sci.utah.edu; xiu.16@osu.edu
https://people.math.osu.edu/xiu.16/（新しいタブで開く）
Department of Mathematics, The Ohio State University, Columbus, 43210 Ohio,
USA
Department of mathematics Ohio State University, Columbus, OH, UNITED STATES, 43210
https://www.osu.edu/（新しいタブで開く）
Journals:
International Journal for Uncertainty Quantification（新しいタブで開く）
Articles:
FAST METHOD FOR HIGH-FREQUENCY ACOUSTIC SCATTERING FROM RANDOM SCATTERERS（新しいタブで開く） - Vol. 1 '2011（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION（新しいタブで開く） - Vol. 11 '2021（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
IMPROVING ACCURACY AND COMPUTATIONAL EFFICIENCY OF OPTIMAL DESIGN OF EXPERIMENTS VIA GREEDY BACKWARD APPROACH（新しいタブで開く） - Vol. 14 '2024（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
INTERACTIVE VISUALIZATION OF PROBABILITY AND CUMULATIVE DENSITY FUNCTIONS （新しいタブで開く） - Vol. 2 '2012（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
STOCHASTIC COLLOCATION ALGORITHMS USING 𝓁1-MINIMIZATION（新しいタブで開く） - Vol. 2 '2012（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
VISUALIZATION OF COVARIANCE AND CROSS-COVARIANCE FIELDS（新しいタブで開く） - Vol. 3 '2013（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
PREFACE: FIRST QUEST CONFERENCE（新しいタブで開く） - Vol. 6 '2016（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
UNCERTAINTY QUANTIFICATION OF SCIENTIFIC PROPOSAL EVALUATIONS（新しいタブで開く） - Vol. 6 '2016（新しいタブで開く） - International Journal for Uncertainty Quantification（新しいタブで開く）
LEARNING REDUCED SYSTEMS VIA DEEP NEURAL NETWORKS WITH MEMORY（新しいタブで開く） - Vol. 1 '2020（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
DEEP LEARNING OF CHAOTIC SYSTEMS FROM PARTIALLY-OBSERVED DATA（新しいタブで開く） - Vol. 3 '2022（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
LEARNING FINE SCALE DYNAMICS FROM COARSE OBSERVATIONS VIA INNER RECURRENCE（新しいタブで開く） - Vol. 3 '2022（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
MODELING UNKNOWN DYNAMICAL SYSTEMS WITH HIDDEN PARAMETERS（新しいタブで開く） - Vol. 3 '2022（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
FLOW MAP LEARNING FOR UNKNOWN DYNAMICAL SYSTEMS: OVERVIEW, IMPLEMENTATION, AND BENCHMARKS（新しいタブで開く） - Vol. 4 '2023（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
MODELING UNKNOWN STOCHASTIC DYNAMICAL SYSTEM VIA AUTOENCODER（新しいタブで開く） - Vol. 5 '2024（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
UNRAVELING CONSUMER PURCHASE JOURNEY USING NEURAL NETWORK MODELS（新しいタブで開く） - Vol. 5 '2024（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
CHEBYSHEV FEATURE NEURAL NETWORK FOR ACCURATE FUNCTION APPROXIMATION（新しいタブで開く） - Vol. 6 '2025（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
DIMENSION-REDUCED RECONSTRUCTION MAP LEARNING FOR PARAMETER ESTIMATION IN LIKELIHOOD-FREE INFERENCE PROBLEMS（新しいタブで開く） - Vol. 6 '2025（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
LEARNING INVERSE MAPS FOR PARAMETER ESTIMATION IN DYNAMICAL SYSTEMS（新しいタブで開く） - Vol. 7 '2026（新しいタブで開く） - Journal of Machine Learning for Modeling and Computing（新しいタブで開く）
Qin, Tong
DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION
Chen, Zhen
DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION
Churchill, Victor
LEARNING INVERSE MAPS FOR PARAMETER ESTIMATION IN DYNAMICAL SYSTEMS
UNRAVELING CONSUMER PURCHASE JOURNEY USING NEURAL NETWORK MODELS
DEEP LEARNING OF CHAOTIC SYSTEMS FROM PARTIALLY-OBSERVED DATA
FLOW MAP LEARNING FOR UNKNOWN DYNAMICAL SYSTEMS: OVERVIEW, IMPLEMENTATION, AND BENCHMARKS
LEARNING FINE SCALE DYNAMICS FROM COARSE OBSERVATIONS VIA INNER RECURRENCE
Taghizadeh, Mehdi
IMPROVING ACCURACY AND COMPUTATIONAL EFFICIENCY OF OPTIMAL DESIGN OF EXPERIMENTS VIA GREEDY BACKWARD APPROACH
Wang, Peng
UNCERTAINTY QUANTIFICATION OF SCIENTIFIC PROPOSAL EVALUATIONS
PREFACE: FIRST QUEST CONFERENCE
Tatsuoka, Caroline
LEARNING INVERSE MAPS FOR PARAMETER ESTIMATION IN DYNAMICAL SYSTEMS
Guo, Ling
STOCHASTIC COLLOCATION ALGORITHMS USING 𝓁1-MINIMIZATION
Jakeman, John D.
DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION
Zhang, Rui
DIMENSION-REDUCED RECONSTRUCTION MAP LEARNING FOR PARAMETER ESTIMATION IN LIKELIHOOD-FREE INFERENCE PROBLEMS
Chang, Lo-Bin
LEARNING REDUCED SYSTEMS VIA DEEP NEURAL NETWORKS WITH MEMORY
MODELING UNKNOWN DYNAMICAL SYSTEMS WITH HIDDEN PARAMETERS
Alemazkoor, Negin
IMPROVING ACCURACY AND COMPUTATIONAL EFFICIENCY OF OPTIMAL DESIGN OF EXPERIMENTS VIA GREEDY BACKWARD APPROACH
Chen, Qifan
MODELING UNKNOWN STOCHASTIC DYNAMICAL SYSTEM VIA AUTOENCODER
Xu, Zhongshu
LEARNING INVERSE MAPS FOR PARAMETER ESTIMATION IN DYNAMICAL SYSTEMS
CHEBYSHEV FEATURE NEURAL NETWORK FOR ACCURATE FUNCTION APPROXIMATION
MODELING UNKNOWN STOCHASTIC DYNAMICAL SYSTEM VIA AUTOENCODER
Tartakovsky, Daniel M.
COMPUTATIONAL FRAMEWORK FOR REAL-TIME DIGITAL TWINS
PREFACE: FIRST QUEST CONFERENCE
Chen, Yuan
CHEBYSHEV FEATURE NEURAL NETWORK FOR ACCURATE FUNCTION APPROXIMATION
MODELING UNKNOWN STOCHASTIC DYNAMICAL SYSTEM VIA AUTOENCODER
Mao, WeiZe
MODELING UNKNOWN DYNAMICAL SYSTEMS WITH HIDDEN PARAMETERS
Potter, Kristin
INTERACTIVE VISUALIZATION OF PROBABILITY AND CUMULATIVE DENSITY FUNCTIONS
Kirby, Robert Mike
VISUALIZATION OF COVARIANCE AND CROSS-COVARIANCE FIELDS
INTERACTIVE VISUALIZATION OF PROBABILITY AND CUMULATIVE DENSITY FUNCTIONS
Yang, Chao
VISUALIZATION OF COVARIANCE AND CROSS-COVARIANCE FIELDS
Shi, Xiaofeng
UNCERTAINTY QUANTIFICATION OF SCIENTIFIC PROPOSAL EVALUATIONS
Chkrebtii, Oksana
DIMENSION-REDUCED RECONSTRUCTION MAP LEARNING FOR PARAMETER ESTIMATION IN LIKELIHOOD-FREE INFERENCE PROBLEMS
Tsuji, Paul
FAST METHOD FOR HIGH-FREQUENCY ACOUSTIC SCATTERING FROM RANDOM SCATTERERS
Yan, Liang
STOCHASTIC COLLOCATION ALGORITHMS USING 𝓁1-MINIMIZATION
Fu, Xiaohan
LEARNING REDUCED SYSTEMS VIA DEEP NEURAL NETWORKS WITH MEMORY
MODELING UNKNOWN DYNAMICAL SYSTEMS WITH HIDDEN PARAMETERS
Li, H. Alice
UNRAVELING CONSUMER PURCHASE JOURNEY USING NEURAL NETWORK MODELS
Johnson, Chris R.
INTERACTIVE VISUALIZATION OF PROBABILITY AND CUMULATIVE DENSITY FUNCTIONS
Ying, Lexing
FAST METHOD FOR HIGH-FREQUENCY ACOUSTIC SCATTERING FROM RANDOM SCATTERERS