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 ...

(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 ...

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 ...

Metal additive manufacturing (AM) experiments are slow and expensive. Engineers are using physics-informed neural networks to predict the outcomes of complex processes involved in AM. The team trained ...

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 ...

Optical coherence tomography (OCT) is an imaging technology that can non-invasively generate cross-sectional images of tissue. OCT is widely used in eye clinics to diagnose and manage retinal diseases ...

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 ...