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Found 4 papers, 2 papers with code
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself.
Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Prediction
In the proposed method, first, we assemble a fusion matrix using the proposed RBF-based fusion approach; second, based on the fused data, we construct spatially and temporally correlated data as inputs for the predictive model; finally, we employ the spatiotemporal graph convolutional network (STGCN) to predict the future trends of some observations.
Kernel-Based Models for Influence Maximization on Graphs based on Gaussian Process Variance Minimization
The inference of novel knowledge, the discovery of hidden patterns, and the uncovering of insights from large amounts of data from a multitude of sources make Data Science (DS) to an art rather than just a mere scientific discipline.
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues
It first surveys the popular machine learning algorithms that are developed in the Julia language.
