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A new deep-learning-based reduced-order modeling (ROM) framework is proposed for application in subsurface flow simulation.
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Z. L. Jin, T. Garipov, O. Volkov, L. J. Durlofsky, Reduced-order modeling of coupled flow-geomechanics problems (SPE paper 193863), in: SPE Reservoir Simulation Conference, Galveston, Texas, USA, 2019
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Y. Liu, W. Sun, L. J. Durlofsky, A deep-learning-based geological parameterization for history matching complex models, Mathematical Geosciences (2019) https://doi.org/10.1007/s11004-019-09794-9
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M. Raissi, P. Perdikaris, G. Karniadakis, Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics 378 (2019) 686–707
2019
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