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Physics-trained AI models speed engineering design and simulations
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 ...
Researchers at Skoltech have proposed a new approach to training neural networks for wave propagation in absorbing media. The ...
Anthropic and OpenAI Group PBC are both preparing to release new flagship LLMs. According to Axios, Meta doesn’t expect its ...
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 ...
Keane, "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks," NBER Working Paper 35037 (2026), ...
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 ...
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 ...
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