Efficient Hierarchical Kriging Modeling Method

Efficient hierarchical Kriging modeling aims to improve the accuracy and speed of spatial interpolation, particularly for high-dimensional or multi-fidelity data, by leveraging the strengths of both traditional Kriging and machine learning techniques. Current research focuses on integrating Kriging with neural networks (e.g., graph convolutional networks, recurrent neural networks), developing novel algorithms like contrastive-prototypical learning to better utilize both neighboring and non-neighboring data, and employing strategies such as increment training and data augmentation to enhance model performance and generalization. These advancements are significant for various applications, including environmental modeling, reservoir characterization, and real-time prediction in engineering, offering more accurate and computationally efficient solutions for spatial data analysis.