Ling Guo

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with George Em Karniadakis

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Publications

2023
[j50]
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Apostolos F. Psaros, Xuhui Meng, Zongren Zou, Ling Guo, George Em Karniadakis:
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons. J. Comput. Phys. 477: 111902 (2023)
[i10]
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  - journals/corr/abs-2303-10913
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Lei Ma, Rong xin Li, Fanhai Zeng, Ling Guo, George Em Karniadakis:
Bi-orthogonal fPINN: A physics-informed neural network method for solving time-dependent stochastic fractional PDEs. CoRR abs/2303.10913 (2023)
2022
[j45]
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  - journals/jcphy/ZhaoMGTK22
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Yue Zhao, Zhiping Mao, Ling Guo, Yifa Tang, George Em Karniadakis:
A spectral method for stochastic fractional PDEs using dynamically-orthogonal/bi-orthogonal decomposition. J. Comput. Phys. 461: 111213 (2022)
[i9]
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  - journals/corr/abs-2201-07766
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Apostolos F. Psaros, Xuhui Meng, Zongren Zou, Ling Guo, George Em Karniadakis:
Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons. CoRR abs/2201.07766 (2022)
2020
[j36]
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  - journals/siamsc/ZhangGK20
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Dongkun Zhang, Ling Guo, George Em Karniadakis:
Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks. SIAM J. Sci. Comput. 42(2): A639-A665 (2020)
2019
[j28]
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Dongkun Zhang, Lu Lu, Ling Guo, George Em Karniadakis:
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems. J. Comput. Phys. 397 (2019)
[j27]
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  - journals/siamsc/GuoZTBK19
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Ling Guo, Fanhai Zeng, Ian W. Turner, Kevin Burrage, George Em Karniadakis:
Efficient Multistep Methods for Tempered Fractional Calculus: Algorithms and Simulations. SIAM J. Sci. Comput. 41(4): A2510-A2535 (2019)
[i4]
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  - journals/corr/abs-1905-01205
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Dongkun Zhang, Ling Guo, George E. Karniadakis:
Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks. CoRR abs/1905.01205 (2019)

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