Nature

Physics-Informed Neural Networks and Differential Equations

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 ...
Forbes

Neural Networks Co-Inventor Geoffrey Hinton Wins Physics Nobel, Fears AI

ANKARA, TURKIYE - OCTOBER 8: An infographic titled "2024 Nobel Prize" created in Ankara, Turkiye on October 8, 2024. 2024 Nobel Prize in physics awarded to John J. Hopfield, Geoffrey E. Hinton for ...
EurekAlert!

New physics-informed neural network for universal and high-fidelity resolution enhancement in fluorescence microscopy

(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 ...
EurekAlert!

Bayesian neural network evaluation method on the neutron-induced fission product yields of Thorium-232

Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...
CNN

‘Godfather of AI’ shares Nobel Prize in physics for work on machine learning

The 2024 Nobel Prize in physics has been awarded to John Hopfield and Geoffrey Hinton for their fundamental discoveries in machine learning, which paved the way for how artificial intelligence is used ...
eWeek

Neural Networks vs. Deep Learning

Binary digits and circuit patterns forming a silhouette of a head. Neural networks and deep learning are closely related artificial intelligence technologies. While they are often used in tandem, ...