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Found 10 papers, 0 papers with code
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net
Furthermore, based on the constructed TgU-net surrogate, a data assimilation method is employed to identify the physical process and parameters simultaneously.
Deep learning based closed-loop optimization of geothermal reservoir production
In this work, we propose a closed-loop optimization framework, based on deep learning surrogates, for the well control optimization of geothermal reservoirs.
Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network
The results show that the deep learning method can provide equivalent upscaling accuracy to the numerical method, and efficiency can be improved significantly compared to numerical upscaling.
Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network
Pressure and saturation are coupled with each other in the governing equations, and thus the two networks are also mutually conditioned in the training process by the discretized governing equations, which also increases the difficulty of model training.
Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling
In order to achieve the theory-guided training, the governing equations of the studied problems can be discretized and the finite difference scheme of the equations can be embedded into the training of CNN.
Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network
The trained neural network can predict solutions of subsurface flow problems with new stochastic parameters.
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
In the proposed framework, a deep neural network that is trained with available data of a physical problem is utilized to generate meta-data and calculate derivatives, and the genetic algorithm is then employed to discover the underlying PDE.
Deep Learning of Subsurface Flow via Theory-guided Neural Network
The TgNN can achieve higher accuracy than the ordinary Artificial Neural Network (ANN) because the former provides physically feasible predictions and can be more readily generalized beyond the regimes covered with the training data.
DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data
However, prior to achieving this goal, major challenges remain to be resolved, including learning PDE under noisy data and limited discrete data.
Identification of physical processes via combined data-driven and data-assimilation methods
Using the training data set, a data-driven method is developed to learn the governing equation of the considered physical problem by identifying the occurred (or dominated) processes and selecting the proper empirical model.
