The application of neural network models to semiconductor device simulation has emerged as a transformative approach in the field of electronics. These models offer significant speed improvements over ...

Keane, "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks," NBER Working Paper 35037 (2026), ...

Biomedical data analysis has evolved rapidly from convolutional neural network-based systems toward transformer architectures and large-scale foundation ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Listen to the first notes of an old, beloved song. Can you name that tune? If you can, congratulations — it’s a triumph of your associative memory, in which one piece of information (the first few ...

When engineers at Sumitomo Riko needed to speed up the design cycle for automotive rubber and polymer components, they turned ...

The idea of simplifying model weights isn’t a completely new one in AI research. For years, researchers have been experimenting with quantization techniques that squeeze their neural network weights ...

Previously met with skepticism, AI won scientists a Nobel Prize for Chemistry in 2024 after they used it to solve the protein folding and design problem, and it has now been adopted by biologists ...

Researchers have developed several data-mechanism hybrid driven methods to improve key variables prediction in process ...

A hunk of material bustles with electrons, one tickling another as they bop around. Quantifying how one particle jostles others in that scrum is so complicated that, beginning in the 1990s, physicists ...