Researchers at Seoul National University and Kyung Hee University report a framework to control collective motions, such as ring, clumps, mill, flock, by training a physics-informed AI to learn the ...
Physics-informed neural networks (PINNs) represent a burgeoning paradigm in computational science, whereby deep learning frameworks are augmented with explicit physical laws to solve both forward and ...
The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...
(A–C) Representative images reconstructed by conventional method (left) and new method (right) of microtubules, nuclear pore complexes and F-actin samples. The regions enclosed by the white boxes are ...
John Hopfield and Geoffrey Hinton won the Nobel Prize in Physics for their work on artificial neural networks and machine learning. Jonathan Nackstrand / AFP via Getty Images A pair of scientists—John ...
As artificial intelligence explodes in popularity, two of its pioneers have nabbed the 2024 Nobel Prize in physics. The prize surprised many, as these developments are typically associated with ...
With work on machine learning that uses artificial neural networks, John J. Hopfield and Geoffrey E. Hinton “showed a completely new way for us to use computers,” the committee said. By Derrick Bryson ...
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