Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (Q2157149)

scientific article

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English	Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network	scientific article

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instance of

scholarly article

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title

Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (English)

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DOI

10.1016/j.jcp.2022.111419

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Journal of Computational Physics

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publication date

21 July 2022

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full work available at URL

https://arxiv.org/abs/2110.10080

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Mathematics Subject Classification ID

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theory-guided convolutional neural network

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surrogate modeling

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inverse modeling

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two-phase flow

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Wikidata QID

Q114163255

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MaRDI profile type

MaRDI publication profile

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OpenAlex ID

W3207319801

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cites work

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Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

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An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems

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PPINN: parareal physics-informed neural network for time-dependent PDEs

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fPINNs: Fractional Physics-Informed Neural Networks

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems

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Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

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Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network

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Theory-guided auto-encoder for surrogate construction and inverse modeling

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Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow

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Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems

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AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

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Mathematics(1 entry)

mardi Publication:2157149