Mathematics and computing articles from across Nature Portfolio

Free boundary problems, such as modelling glacier melt, are difficult to capture with neural operators. A new framework addresses this challenge by leveraging the mathematical principle of topological conjugacy.

Explaining the global decision logic of black-box medical artificial intelligence (AI) models is a formidable challenge. We proposed class-association manifold learning, an explanatory framework that harnesses low-dimensional manifolds to visualize and accurately explore hidden global decision rules captured by medical AI models. Class-association manifold learning enabled human-interpretable medical knowledge discovery while ensuring AI model alignment.

A simple machine-learning algorithm, inspired by honeybees’ visual memory and sense of direction, enables a flying robot to pinpoint its home location.

Nature Computational Science now takes part in the co-review initiative in order to encourage the involvement of early-career researchers in the peer review process and to ensure proper recognition of their service.

Three humanities scholars with limited coding experience created an AI research platform in a matter of days. Here is what they built, what they learnt and what to watch out for.

What happens when AI companies produce models that they say the public can’t have — and how should users and governments react?

The late Hungarian mathematician Paul Erdős thought he had the last word on a geometry problem. Now an OpenAI chatbot has proved him wrong.

Free boundary problems, such as modelling glacier melt, are difficult to capture with neural operators. A new framework addresses this challenge by leveraging the mathematical principle of topological conjugacy.