research
∙
04/15/2022
Deep learning based closed-loop optimization of geothermal reservoir production
To maximize the economic benefits of geothermal energy production, it is...
share
research
∙
12/31/2021
Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network
Large-scale or high-resolution geologic models usually comprise a huge n...
research
∙
11/14/2021
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network
We build surrogate models for dynamic 3D subsurface single-phase flow pr...
research
∙
12/11/2020
Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
Machine learning models have been successfully used in many scientific a...
research
∙
11/24/2020
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data
Data-driven discovery of partial differential equations (PDEs) has attra...
research
∙
11/17/2020
Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling
A Theory-guided Auto-Encoder (TgAE) framework is proposed for surrogate ...
research
∙
09/08/2020
Weak Form Theory-guided Neural Network (TgNN-wf) for Deep Learning of Subsurface Single and Two-phase Flow
Deep neural networks (DNNs) are widely used as surrogate models in geoph...
research
∙
08/24/2020
A Lagrangian Dual-based Theory-guided Deep Neural Network
The theory-guided neural network (TgNN) is a kind of method which improv...
research
∙
04/25/2020
