A-Z Index
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/(open in a new tab)
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/(open in a new tab)
Journals:
International Journal for Uncertainty Quantification(open in a new tab)
Articles:
FAST METHOD FOR HIGH-FREQUENCY ACOUSTIC SCATTERING FROM RANDOM SCATTERERS(open in a new tab) - Vol. 1 '2011(open in a new tab) - International Journal for Uncertainty Quantification(open in a new tab)
DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION(open in a new tab) - Vol. 11 '2021(open in a new tab) - International Journal for Uncertainty Quantification(open in a new tab)
IMPROVING ACCURACY AND COMPUTATIONAL EFFICIENCY OF OPTIMAL DESIGN OF EXPERIMENTS VIA GREEDY BACKWARD APPROACH(open in a new tab) - Vol. 14 '2024(open in a new tab) - International Journal for Uncertainty Quantification(open in a new tab)
INTERACTIVE VISUALIZATION OF PROBABILITY AND CUMULATIVE DENSITY FUNCTIONS (open in a new tab) - Vol. 2 '2012(open in a new tab) - International Journal for Uncertainty Quantification(open in a new tab)
STOCHASTIC COLLOCATION ALGORITHMS USING 𝓁1-MINIMIZATION(open in a new tab) - Vol. 2 '2012(open in a new tab) - International Journal for Uncertainty Quantification(open in a new tab)
VISUALIZATION OF COVARIANCE AND CROSS-COVARIANCE FIELDS(open in a new tab) - Vol. 3 '2013(open in a new tab) - International Journal for Uncertainty Quantification(open in a new tab)
PREFACE: FIRST QUEST CONFERENCE(open in a new tab) - Vol. 6 '2016(open in a new tab) - International Journal for Uncertainty Quantification(open in a new tab)
UNCERTAINTY QUANTIFICATION OF SCIENTIFIC PROPOSAL EVALUATIONS(open in a new tab) - Vol. 6 '2016(open in a new tab) - International Journal for Uncertainty Quantification(open in a new tab)
LEARNING REDUCED SYSTEMS VIA DEEP NEURAL NETWORKS WITH MEMORY(open in a new tab) - Vol. 1 '2020(open in a new tab) - Journal of Machine Learning for Modeling and Computing(open in a new tab)
DEEP LEARNING OF CHAOTIC SYSTEMS FROM PARTIALLY-OBSERVED DATA(open in a new tab) - Vol. 3 '2022(open in a new tab) - Journal of Machine Learning for Modeling and Computing(open in a new tab)
LEARNING FINE SCALE DYNAMICS FROM COARSE OBSERVATIONS VIA INNER RECURRENCE(open in a new tab) - Vol. 3 '2022(open in a new tab) - Journal of Machine Learning for Modeling and Computing(open in a new tab)
MODELING UNKNOWN DYNAMICAL SYSTEMS WITH HIDDEN PARAMETERS(open in a new tab) - Vol. 3 '2022(open in a new tab) - Journal of Machine Learning for Modeling and Computing(open in a new tab)
FLOW MAP LEARNING FOR UNKNOWN DYNAMICAL SYSTEMS: OVERVIEW, IMPLEMENTATION, AND BENCHMARKS(open in a new tab) - Vol. 4 '2023(open in a new tab) - Journal of Machine Learning for Modeling and Computing(open in a new tab)
MODELING UNKNOWN STOCHASTIC DYNAMICAL SYSTEM VIA AUTOENCODER(open in a new tab) - Vol. 5 '2024(open in a new tab) - Journal of Machine Learning for Modeling and Computing(open in a new tab)
UNRAVELING CONSUMER PURCHASE JOURNEY USING NEURAL NETWORK MODELS(open in a new tab) - Vol. 5 '2024(open in a new tab) - Journal of Machine Learning for Modeling and Computing(open in a new tab)
CHEBYSHEV FEATURE NEURAL NETWORK FOR ACCURATE FUNCTION APPROXIMATION(open in a new tab) - Vol. 6 '2025(open in a new tab) - Journal of Machine Learning for Modeling and Computing(open in a new tab)
DIMENSION-REDUCED RECONSTRUCTION MAP LEARNING FOR PARAMETER ESTIMATION IN LIKELIHOOD-FREE INFERENCE PROBLEMS(open in a new tab) - Vol. 6 '2025(open in a new tab) - Journal of Machine Learning for Modeling and Computing(open in a new tab)
LEARNING INVERSE MAPS FOR PARAMETER ESTIMATION IN DYNAMICAL SYSTEMS(open in a new tab) - Vol. 7 '2026(open in a new tab) - Journal of Machine Learning for Modeling and Computing(open in a new tab)
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