A research team from the Xinjiang Astronomical Observatory (XAO) of the Chinese Academy of Sciences has developed an ...

Researchers at Shanghai University have developed a physics-constrained, data-efficient artificial intelligence framework ...

Neural phase retrieval (NeuPh) employs a CNN-based encoder to learn measurement-specific information and encode them into a latent-space representation. The MLP decoder reconstructs the phase values ...

An MIT spinoff co-founded by robotics luminary Daniela Rus aims to build general-purpose AI systems powered by a relatively new type of AI model called a liquid neural network. The spinoff, aptly ...

Scientists have developed a floating PV digital twin system, trained on data from 155 physical experiments, using a two-tier artificial neural network (ANN) with a high-fidelity model and a ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...

This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

Organoid Intelligence (OI) represents a groundbreaking convergence of biology and technology, aiming to redefine biocomputing using brain organoids—three-dimensional neural structures derived from ...