Researchers in China have created a dataset of various PV faults and normalized it to accommodate different array sizes and typologies. After testing the new approach in combination with the 1D-CNN ...

MIT researchers designed a computationally efficient algorithm for machine learning with symmetric data that also requires fewer data for training than conventional approaches. Their work could inform ...

Particle physics may have been an early adopter, but AI has now spread throughout physics. This shouldn’t be too surprising. Physics is data-heavy and computationally intensive, so it benefits from ...

A new international study has introduced Curved Neural Networks—a new type of AI memory architecture inspired by ideas from ...

“ORNL is leading the AI frontier in science,” Potok concluded. “We are using AI to simulate, predict and accelerate ...

Automated Model Generation (AMG) algorithms are an important technique for creating Artificial Neural Network (ANN) models in microwave design automation. AMG integrates all the subtasks in ANN ...

Decision-making often involves trial and error, but conventional models assume we always act optimally based on past experience.

Using this information, the model can then tell us the probability of a drug-protein interaction that we did not previously have in the database, as the algorithms can efficiently analyse large ...

Signal processing has made extensive use of neural networks. This study examines neural network training using the PSO and BP algorithms. To train the neural network, the PSO algorithm alone, the BP ...