Teaching physics to neural networks enables those networks to better adapt to chaos within their environment. The work has implications for improved artificial intelligence (AI) applications ranging ...

Suppose you have a thousand-page book, but each page has only a single line of text. You’re supposed to extract the information contained in the book using a scanner, only this particular scanner ...

Suppose you have a thousand-page book, but each page has only a single line of text. You’re supposed to extract the information contained in the book using a scanner, only this particular scanner ...

FAYETTEVILLE, GA, UNITED STATES, December 31, 2025 /EINPresswire.com/ — Artificial intelligence (AI) is increasingly transforming computational mechanics, yet many AI-driven models remain limited by ...

Deep thinkers John Hopfield and Geoffrey Hinton share the 2024 Nobel Prize for Physics for their work on machine learning. (Courtesy: Ill. Niklas Elmehed © Nobel ...

Artificial neural networks are a form of machine-learning algorithm with a structure roughly based on that of the human brain. Like other kinds of machine-­learning ...

It shows the schematic of the physics-informed neural network algorithm for pricing European options under the Heston model.  The market price of risk is taken to be λ=0. Automatic differentiation is ...

Biology-inspired, silicon-based computing may boost AI efficiency; AMP2 instead uses AI to accelerate anaerobic biology.