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Found 7 papers, 1 papers with code
Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework
However, when facing the complex real-world, most of the existing methods still strongly rely on the quantity and quality of observation data.
Stabilizing the Maximal Entropy Moment Method for Rarefied Gas Dynamics at Single-Precision
This paper aims to stabilize MEM, making it possible to simulating very strong normal shock waves on modern GPUs at single precision.
Multi-scale data reconstruction of turbulent rotating flows with Gappy POD, Extended POD and Generative Adversarial Networks
Different gap sizes and gap geometries are investigated in order to assess the importance of coherency and multi-scale properties of the missing information.
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, link prediction, and graph classification.
Data-Driven Constitutive Relation Reveals Scaling Law for Hydrodynamic Transport Coefficients
We further proposed a constitutive relation model based on scaling law and tested it on the calculation of Rayleigh scattering spectra.
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning
Graph representation learning has long been an important yet challenging task for various real-world applications.
Heterogeneous-Temporal Graph Convolutional Networks: Make the Community Detection Much Better
Community detection has long been an important yet challenging task to analyze complex networks with a focus on detecting topological structures of graph data.
