Physics-informed neural networks (PINNs) have shown remarkable prospects in solving forward and inverse problems involving partial differential equations (PDEs). But they often stumble when ...

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 is revolutionizing physics by making complex concepts more intuitive, interactive, and personalized. From physics-informed neural networks to AI-powered simulations, these ...

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 ...

Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily powerful, yet their internal workings remain largely a "black box." To better ...

AI models trained on physics are slashing the time needed for complex engineering simulations, enabling faster design iterations across industries like automotive, aerospace, and materials science. By ...