We also shared the results of implementing a model from scratch following DeepMind’s theoretical basis. Based on our results, we recommend Graph Neural Networks for physics simulation workloads. The ...

At temperatures of about 100 million degrees, different kinds of turbulence arise within the plasma, and physics-based numerical models that characterize that behavior are very slow. We’re developing ...

When did you first try to combine physics with deep learning? Rose Yu: It started with traffic. I was a grad student at USC, and the campus is right near the intersection of I-10 and I-110.

Self-supervised AI learns physics to reconstruct microscopic images from holograms Advance uses thought experiments, instead of real data, to expedite learning ...

NeuralGCM is also orders of magnitude faster and cheaper computationally than traditional physics-based climate models. Our 1.4° model is >3,500-times faster than X-SHiELD, meaning if researchers ...

The prize goes to John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks,” the Royal Swedish Academy of ...