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Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the amount of carbon dioxide in the atmosphere, which also places high demands on reliability.
Petroleum reservoir simulation
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Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
T. Chen and H. Chen · 1995
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Theory of Gas Injection Processes
F. M. Orr et al · 2007
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CO 2 sequestration in deep sedimentary formations
S. M. Benson and D. R. Cole · 2008
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Computational methods for multiphase flow
A. Prosperetti and G. Tryggvason · 2009
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Development and application of reduced-order modeling procedures for subsurface flow simulation
M. A. Cardoso, L. J. Durlofsky, and P. Sarma · 2009
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Applied Geostatistics with SGeMS: A User’s Guide
N. Remy, A. Boucher, and J. Wu · 2009
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Turbulent dispersed multiphase flow
S. Balachandar and J. K. Eaton · 2010
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Investigation of CO 2 plume behavior for a large-scale pilot test of geologic carbon storage in a saline formation
C. Doughty · 2010
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Numerical simulation of gas migration through engineered and geological barriers for a deep repository for radioactive waste
B. Amaziane, M. El Ossmani, and M. Jurak · 2012
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Review of surrogate modeling in water resources
S. Razavi, B. A. Tolson, and D. H. Burn · 2012
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Climate change 2014: Synthesis report. contribution of working groups i, ii and iii to the fifth assessment report of the intergovernmental panel on climate change
R. K. Pachauri and L. A. Meyer · 2014
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CO 2 plume tracking and history matching using multilevel pressure monitoring at the illinois basin – decatur project
C. W. Strandli, E. Mehnert, and S. M. Benson · 2014
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Mechanisms for geological carbon sequestration
D. Zhang and J. Song · 2014
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Persistent questions of heterogeneity, uncertainty, and scale in subsurface flow and transport
P. K. Kitanidis · 2015
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Surrogate accelerated sampling of reservoir models with complex structures using sparse polynomial chaos expansion
H. Bazargan, M. Christie, A. H. Elsheikh, and M. Ahmadi · 2015
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Varying index coefficient models
Shujie Ma and Peter X-K Song · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Localized lattice Boltzmann equation model for simulating miscible viscous displacement in porous media
X. Meng and Z. Guo · 2016
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Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification
Y. Zhu and N. Zabaras · 2018
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CO 2 plume migration and dissolution in layered reservoirs
G. Wen and S. M. Benson · 2019
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Deep convolutional encoder‐decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
S. Mo, Y. Zhu, N. Zabaras, X. Shi, and J. Wu · 2019
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Predicting CO 2 plume migration in heterogeneous formations using conditional deep convolutional generative adversarial network
Z. Zhong, A. Y. Sun, and H. Jeong · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G. E. Karniadakis · 2019
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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Y. Zhu, N. Zabaras, P.-S. Koutsourelakis, and P. Perdikaris · 2019
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fPINNs: Fractional physics-informed neural networks
G. Pang, L. Lu, and G. E. Karniadakis · 2019
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Continuous relative permeability model for compositional simulation
O. Khebzegga, A. Iranshahr, and H. Tchelepi · 2020
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Machine learning in geo- and environmental sciences: From small to large scale
P. Tahmasebi, S. Kamrava, T. Bai, and M. Sahimi · 2020
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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
M. Tang, Y. Liu, and L. J. Durlofsky · 2020
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Physics-informed neural networks for inverse problems in nano-optics and metamaterials
Y. Chen, L. Lu, G. E. Karniadakis, and L. Dal Negro · 2020
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Systems biology informed deep learning for inferring parameters and hidden dynamics
Learning nonlinear operators via deeponet based on the universal approximation theorem of operators
L. Lu, P. Jin, G. Pang, Z. Zhang, and G. E. Karniadakis · 2021
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Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification
Ilja Kröker and Sergey Oladyshkin · 2022
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U-FNO—an enhanced fourier neural operator-based deep-learning model for multiphase flow
G. Wen, Z. Li, K. Azizzadenesheli, A. Anandkumar, and S. M. Benson · 2022
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Accelerating carbon capture and storage modeling using fourier neural operators
G. Wen, Z. Li, Q. Long, K. Azizzadenesheli, A. Anandkumar, and S. M. Benson · 2022
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A. Yazdani, L. Lu, M. Raissi, and G. E. Karniadakis · 2020
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Physics based deep learning for nonlinear two-phase flow in porous media
O. Fuks and H. Tchelepi · 2020
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Fourier neural operator for parametric partial differential equations
Z. Li, N. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. Stuart, and A. Anandkumar · 2020
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Pore-scale modelling and sensitivity analyses of hydrogen-brine multiphase flow in geological porous media
L. Hashemi, M. Blunt, and H. Hajibeygi · 2021
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B-splines on sparse grids for surrogates in uncertainty quantification
Michael F Rehme, Fabian Franzelin, and Dirk Pflüger · 2021
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CCSNet: A deep learning modeling suite for CO 2 storage
G. Wen, C. Hay, and S. M. Benson · 2021
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Uncertainty quantification for multiphase-cfd simulations of bubbly flows: a machine learning-based bayesian approach supported by high-resolution experiments
Yang Liu, Dewei Wang, Xiaodong Sun, Nam Dinh, and Rui Hu · 2021
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X.-Y. Liu, H. Sun, M. Zhu, L. Lu, and J.-X. Wang · 2022
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Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
J. Yu, L. Lu, X. Meng, and G. E. Karniadakis · 2022
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M. Daneker, Z. Zhang, G. E. Karniadakis, and L. Lu · 2022
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Prediction of porous media fluid flow using physics informed neural networks
M. M. Almajid and M. O Abu-Al-Saud · 2022
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A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data
L. Lu, X. Meng, S. Cai, Z. Mao, S. Goswami, Z. Zhang, and G. E. Karniadakis · 2022
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Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems
M. Yin, E. Zhang, Y. Yu, and G. E. Karniadakis · 2022
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A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials
S. Goswami, M. Yin, Y. Yu, and G. E. Karniadakis · 2022
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MIONet: Learning multiple-input operators via tensor product
P. Jin, S. Meng, and L. Lu · 2022
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NOMAD: Nonlinear manifold decoders for operator learning
J. Seidman, G. Kissas, P. Perdikaris, and G. J. Pappas · 2022
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Adaptive machine learning with physics-based simulations for mean time to failure prediction of engineering systems
Hao Wu, Yanwen Xu, Zheng Liu, Yumeng Li, and Pingfeng Wang · 2023
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Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
M. Zhu, S. Feng, Y. Lin, and L. Lu · 2023
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Deep learning for solving and estimating dynamic macro-finance models
B. Fan, E. Qiao, A. Jiao, Z. Gu, W. Li, and L. Lu · 2023
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A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
C. Wu, M. Zhu, Q. Tan, Y. Kartha, and L. Lu · 2023
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Reliable extrapolation of deep neural operators informed by physics or sparse observations
M. Zhu, H. Zhang, A. Jiao, G. E. Karniadakis, and L. Lu · 2023
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Modified varying index coefficient autoregression model for representation of the nonstationary vibration from a planetary gearbox
Yuejian Chen, Meng Rao, Ke Feng, and Gang Niu · 2023
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PPDONet: Deep operator networks for fast prediction of steady-state solutions in disk–planet systems
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Accident data-driven human fatigue analysis in maritime transport using machine learning
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A data-driven risk assessment of arctic maritime incidents: Using machine learning to predict incident types and identify risk factors
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Simulation-free reliability analysis with importance sampling-based adaptive training physics-informed neural networks: Method and application to chloride penetration
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