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 systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily ...

(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 ...

Now, artificial intelligence (AI) tools are providing powerful new ways to address long-standing problems in physics. “The ...

Physics-trained AI models are accelerating engineering simulations by replacing or supplementing traditional solvers, enabling rapid design iteration in industries like automotive and aerospace. These ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...