A recent addition to the toolbox of machine learning models for chemistry and materials science are graph neural networks (GNNs), which operate on graph-structured data and have strong ties to the ...

In this article, we propose Neural Discovery of Network Dynam- ics (ND2), a deep learning method that automatically discovers net- work dynamics formulas through symbolic regression.

Physics-informed convolutional neural networks (PICNNs) have emerged as a powerful extension of physics-informed neural networks (PINNs), offering superior generalization and efficiency for ...

Fourth, neural operators are democratizing science as running the trained neural operators does not require the deep domain expertise needed to set up and run many traditional solvers.

Physical neural networks (PNNs) are a class of neural-like networks that make use of analogue physical systems to perform computations. Although at present confined to small-scale laboratory ...

Fundamental component of graph neural networks that iteratively aggregates and updates the features from neighbouring nodes, enabling the propagation of information throughout the graph structure.

Scaling up for end-to-end on-chip photonic neural network inference Bo Wu1, Chaoran Huang 1 2, Jialong Zhang1, Hailong Zhou 1 , Yilun Wang 1, Jianji Dong and

In the second phase, the two concept spaces were aligned with a translation module, that is, a neural network establishing a map from teacher con-cept space to student concept space, which was ...

In a related line of work, several studies tied the structure of neural cor- relations and population-level statistics to the information content of neural codes25–29.

FFM learning shows training the deepest optical neural networks with millions of parameters achieves accuracy equivalent to the ideal model.