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 ...

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 ...

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 ...

When engineers at Sumitomo Riko needed to speed up the design cycle for automotive rubber and polymer components, they turned to AI models trained not just on data but on the fundamental equations of ...

An improved model identifies power-reducing dust accumulation on photovoltaic modules, helping engineers know when the modules need cleaning.

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 ...

The market presents opportunities in digital transformation, deep learning, real-time analytics, and AI-driven optimization ...

A growing body of academic research shows that the internal architecture of deep neural networks running on NVIDIA GPUs can ...

This article discusses legal issues surrounding the harms caused by artificial intelligence, and where liability may lie if a neural network is not functioning correctly in the real world.Artificial ...