Search Results for author:
Found 10 papers, 1 papers with code
Spectral Informed Neural Network: An Efficient and Low-Memory PINN
With growing investigations into solving partial differential equations by physics-informed neural networks (PINNs), more accurate and efficient PINNs are required to meet the practical demands of scientific computing.
Early Risk Assessment Model for ICA Timing Strategy in Unstable Angina Patients Using Multi-Modal Machine Learning
The challenge lies in determining the optimal timing for ICA in UA patients, balancing the need for revascularization in high-risk patients against the potential complications in low-risk ones.
Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression
In this study, we introduce a genetic symbolic regression (SR) algorithm to discover an interpretable mathematical expression for the mean velocity deficit throughout the wake, a previously unavailable insight.
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.
