As semiconductor technologies advance, device structures are becoming increasingly complex. New materials and architectures introduce intricate physical effects requiring accurate modeling to ensure ...

Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.

Researchers from Carnegie Mellon University and Los Alamos National Laboratory have used machine learning to create a model that can simulate reactive processes in a diverse set of organic materials ...

Physiologically Based Pharmacokinetic Model to Assess the Drug-Drug-Gene Interaction Potential of Belzutifan in Combination With Cyclin-Dependent Kinase 4/6 Inhibitors A total of 14,177 patients were ...

Spiking Neural Networks (SNNs) represent the "third generation" of neural models, capturing the discrete, asynchronous, and energy-efficient nature of ...

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

A rotating cylinder with its side cut away to expose the core, showing patches of purple, blue, green, yellow, and orange that are dense in the middle and more diffuse toward the edges. This rotating ...

For more than a decade, neuroscientists had recordings from monkey brains learning to sort visual patterns. They had analyzed the data, published landmark papers, and moved on. Then a computer model ...

Combining Machine Learning Models a ...