Neural networks have emerged as a powerful framework for addressing complex problems across numerous scientific domains. In particular, the interplay between neural network models and constraint ...

Recent advances at the intersection of neural networks and inverse scattering problems have transformed traditional approaches to imaging and material characterisation. Inverse scattering involves ...

Deep neural networks have gained fame for their capability to process visual information. And in the past few years, they have become a key component of many computer vision applications. Among the ...

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead ...

Differential equations are fundamental tools in physics: they are used to describe phenomena ranging from fluid dynamics to general relativity. But when these equations become stiff (i.e. they involve ...

Generative artificial intelligence (AI) — such as ChatGPT and Dalle-2 — is undoubtedly one of the most groundbreaking and discussed technologies in recent history. Its applications and related issues ...

Binary digits and circuit patterns forming a silhouette of a head. Neural networks and deep learning are closely related artificial intelligence technologies. While they are often used in tandem, ...

Deep neural networks have a huge advantage: They replace “feature engineering”—a difficult and arduous part of the classic machine learning cycle—with an end-to-end process that automatically learns ...

Deep neural networks have gained fame for their capability to process visual information. And in the past few years, they have become a key component of many computer vision applications. Among the ...

A landmark has been reached in the field of physics and artificial intelligence with the successful resolution of a 50-year-old science problem. The neural network, developed by Alphabet subsidiary, ...