AlphaGalileo

Deep learning meets plant physics to improve non-destructive nitrogen monitoring in crops

A research team has developed a new hybrid artificial intelligence framework that can accurately estimate leaf nitrogen ...
GitHub

transformerlab-inference

The training and evaluation code for state-of-the-art models (e.g., Vicuna, MT-Bench). A distributed multi-model serving system with web UI and OpenAI-compatible RESTful APIs. See more command options ...
IEEE

PIGAT: Physics-Informed Graph Attention Transformer for Air Traffic State Prediction

Abstract: Efficient and resilient traffic management relies on accurate prediction of air traffic states. However, the complex spatial-temporal dependencies of air traffic networks make this task ...

The factory workers who build the power grid by hand

The large transformers needed to power the AI boom are made partly by hand and take four to six weeks to build, leading to a ...
IEEE

Physics-Informed Machine Learning for Transformer Condition Monitoring – Part II: Physics-Informed Neural Networks and Uncertainty Quantification

Physics-informed Neural Networks (PINNs) have emerged as a promising approach to address some of the limitations of classical Neural Networks (NNs) in science and engineering applications. In ...
Morning Overview on MSN

AI predicts solar wind with record accuracy to protect satellites

Artificial intelligence is quietly rewriting the rules of space weather forecasting, turning what used to be a hazy two-day ...
GitHub

[When Fast Fourier Transform Meets Transformer for Image Restoration] (ECCV 2024)

Thanks for your interest in our work, we will continue to optimize our code. If you have any other questions, please feel free to raise them in the issues, and I will try my best to address them!