Morning Overview on MSN
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 advent of high-density recording technologies, such as Neuropixels and large-scale calcium imaging, has provided an unprecedented look into the ...
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), ...
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 ...
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 ...
Researchers at Skoltech have proposed a new approach to training neural networks for wave propagation in absorbing media. The ...
Researchers have developed several data-mechanism hybrid driven methods to improve key variables prediction in process ...
Morning Overview on MSN
Physics-trained AI models speed up engineering simulations and design work
Running a single physics simulation can take hours or days, depending on the complexity of the geometry and the equations ...
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