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Domain adaption and physical constrains transfer learning for shale gas production
To tackle these problems, we propose a novel transfer learning methodology that utilizes domain adaptation and physical constraints.
Ensemble Interpretation: A Unified Method for Interpretable Machine Learning
To address the issues of stability and fidelity in interpretable learning, a novel interpretable methodology, ensemble interpretation, is presented in this paper which integrates multi-perspective explanation of various interpretation methods.
Has China caught up to the US in AI research? An exploration of mimetic isomorphism as a model for late industrializers
Artificial Intelligence (AI), a cornerstone of 21st-century technology, has seen remarkable growth in China.
Prediction of single well production rate in water-flooding oil fields driven by the fusion of static, temporal and spatial information
In this study, a novel machine learning model is constructed to fuse the static geological information, dynamic well production history, and spatial information of the adjacent water injection wells.
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing
Machine learning approaches are widely studied in the production prediction of CBM wells after hydraulic fracturing, but merely used in practice due to the low generalization ability and the lack of interpretability.
References of References: How Far is the Knowledge Ancestry
Scientometrics studies have extended from direct citations to high-order citations, as simple citation count is found to tell only part of the story regarding scientific impact.
Information Retrieval
Digital Libraries
