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 ...

Artificial intelligence (AI) systems, particularly artificial neural networks, have proved to be highly promising tools for ...

(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 ...

Suppose you have a thousand-page book, but each page has only a single line of text. You’re supposed to extract the information contained in the book using a scanner, only this particular scanner ...

One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...

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 ...