Nature

Physics-informed AI framework for multiscale design, discovery, and optimization of polymer nanocomposite microstructures using neural operators

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...
GitHub

A PyTorch implementation of Signed Graph Convolutional Network (ICDM 2018).

--edge-path STR Input graph path. Default is `input/bitcoin_otc.csv`. --features-path STR Features path. Default is `input/bitcoin_otc.csv`. --embedding-path STR ...
Nature

A targeted one dimensional fully convolutional autoencoder network for intelligent compression of magnetic flux leakage data

In response to the issue of massive data volume generated by magnetic flux leakage (MFL) non-destructive testing in oil and gas pipelines, an intelligent data compression method based on a targeted ...
Scientific Research Publishing

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder ()

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the ...
GitHub

GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.

GCA-ROM requires pytorch, pyg, matplotlib, scipy and h5py. They can be easily installed via pip or conda. In the notebook folder, one can find the *.ipynb files ...
Frontiers

A Markov Regime Switching Model for Ultra-Short-Term Wind Power Prediction Based on Toeplitz Inverse Covariance Clustering

The rapid development of wind energy has brought a lot of uncertainty to the power system. The accurate ultra-short-term wind power prediction is the key issue to ensure the stable and economical ...
Frontiers

Downscaling Satellite and Reanalysis Precipitation Products Using Attention-Based Deep Convolutional Neural Nets

High-quality and high-resolution precipitation products are critically important to many hydrological applications. Advances in satellite remote sensing instruments and data retrieval algorithms ...