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 ...

Future events such as the weather or satellite trajectories are computed in tiny time steps, so the computation must be both efficient and as accurate as possible at each step lest errors pile up. A ...

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 ...

STOCKHOLM — John Hopfield and Geoffrey Hinton were awarded the Nobel Prize in physics Tuesday for discoveries and inventions that formed the building blocks of machine learning. "This year's two Nobel ...

(a) Neural network architecture; (b) nonlinear Schrödinger equation simulation for the data generation and analysis; (c) experimental setup of the mode-locked fiber lasers; (d) comparison of ...

Using artificial intelligence, researchers at the RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences, ...

Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries? Theoretical ...

Don’t know your convolutional neural networks from your boosted decision trees? Symmetry is here to help. It’s time for some deep learning. Check out this list to pick up some new terminology—and ...

Boltzmann generators overcome sampling problems between long-lived states. The Boltzmann generator works as follows: 1. We sample from a simple (e.g., Gaussian) distribution. 2. An invertible deep ...