| Reproducing kernels of generalized Sobolev spaces via a Green function approach with distributional operators GE Fasshauer, Q Ye Numerische Mathematik 119, 585-611, 2011 | 73 | 2011 |
| Generalized Mercer Kernels and Reproducing Kernel Banach Spaces Y Xu, Q Ye Memoirs of the American Mathematical Society 258 (1243), 2019 | 71 | 2019 |
| Approximation of stochastic partial differential equations by a kernel-based collocation method I Cialenco, GE Fasshauer, Q Ye International Journal of Computer Mathematics 89 (18), 2543-2561, 2012 | 65 | 2012 |
| Solving support vector machines in reproducing kernel Banach spaces with positive definite functions GE Fasshauer, FJ Hickernell, Q Ye Applied and Computational Harmonic Analysis 38 (1), 115-139, 2015 | 55 | 2015 |
| Reproducing kernels of Sobolev spaces via a green kernel approach with differential operators and boundary operators GE Fasshauer, Q Ye Advances in Computational Mathematics 38, 891-921, 2013 | 38 | 2013 |
| Kernel-based collocation methods versus Galerkin finite element methods for approximating elliptic stochastic partial differential equations GE Fasshauer, Q Ye Meshfree Methods for Partial Differential Equations VI, 155-170, 2013 | 20 | 2013 |
| A kernel-based collocation method for elliptic partial differential equations with random coefficients GE Fasshauer, Q Ye Monte Carlo and Quasi-Monte Carlo Methods 2012, 331-347, 2013 | 19 | 2013 |
| Analyzing reproducing kernel approximation methods via a Green function approach Q Ye Illinois Institute of Technology, 2012 | 17 | 2012 |
| Kernel-based learning methods for stochastic partial differential equations Q Ye Engineering Analysis with Boundary Elements 169, 105960, 2024 | 13* | 2024 |
| Optimal Designs of Positive Definite Kernels for Scattered Data Approximation Q Ye Applied and Computational Harmonic Analysis, 0 | 13* | |
| TGM-Nets: A deep learning framework for enhanced forecasting of tumor growth by integrating imaging and modeling Q Chen, Q Ye, W Zhang, H Li, X Zheng Engineering Applications of Artificial Intelligence 126, 106867, 2023 | 12 | 2023 |
| Generalized representer theorems in Banach spaces L Huang, C Liu, L Tan, Q Ye Analysis and Applications 19 (01), 125-146, 2021 | 12 | 2021 |
| Approximation of nonlinear stochastic partial differential equations by a kernel-based collocation method Q Ye International Journal of Applied Nonlinear Science 3 1 (2), 156-172, 2014 | 10 | 2014 |
| Sparse support vector machines in reproducing kernel Banach spaces Z Li, X Yuesheng, Q Ye Contemporary Computational Mathematics - A Celebration of the 80th Birthday …, 0 | 8* | |
| Support vector machines in reproducing kernel Hilbert spaces versus Banach spaces Q Ye Approximation Theory XIV: San Antonio 2013, 377-395, 2014 | 7 | 2014 |
| Support vector machine classifiers by non-Euclidean margins. Y Lin, Q Ye Mathematical Foundations of Computing 3 (4), 2020 | 6 | 2020 |
| Reproducing kernels of generalized Sobolev spaces via a Green function approach with differential operators Q Ye arXiv preprint arXiv:1109.0109, 2011 | 6 | 2011 |
| Kernel-based probability measures for generalized interpolations: A deterministic or stochastic problem? Q Ye Journal of Mathematical Analysis and Applications 477 (1), 420-436, 2019 | 4 | 2019 |
| Generalizations of simple kriging methods in spatial data analysis Q Ye Meshfree Methods for Partial Differential Equations VIII, 145-166, 2017 | 4 | 2017 |
| Positive definite multi-kernels for scattered data interpolations Q Ye Applied and Computational Harmonic Analysis 62, 251-260, 2023 | 3 | 2023 |
Yuesheng XuProfessor of Data Science & Math, Old Dominion University; Professor Emeritus, Syracuse UniversityVerifisert e-postadresse på odu.edu
