Rose Yu has drawn on the principles of fluid dynamics to improve deep learning systems that predict traffic, model the ...

A more widely used type of network is the recurrent neural network, in which data can flow in multiple directions. These neural networks possess greater learning abilities and are widely employed ...

It now appears that neural nets may be the next frontier in the advance of computing technology as a whole. But what are neural nets? How do they work? In this article, we'll give an overview of the ...

In an article published in the journal Machine Learning and Technology, researchers investigated using physics-informed neural networks ... data impacted PINN prediction accuracy for flow features ...

Neural Networks use classifiers, which are algorithms that map the input data to a specific category. For instance, for a classifier, y = f*(x) maps the input x to the category y. MLP determines ...

As a result, researchers are increasingly turning to synthetic data to supplement or even replace natural data for training neural networks. “Machine learning has long been struggling with the data ...

Mohamad Hassoun, author of Fundamentals of Artificial Neural Networks (MIT Press ... but where training data is available. If you're enjoying this article, consider supporting our award-winning ...