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 ...

Researchers at Skoltech have proposed a new approach to training neural networks for wave propagation in absorbing media. The ...

A new research model called PiGRAND merges physics guidance with graph neural diffusion to predict and control AM processes.

(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 ...

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 ...

Short video shows the neural network training results and reproduction of flocking from real-world data. Credit: Cell Reports Physical Science Learning local rules with physics-informed AI To address ...