Categories: AI/ML News

AI extracts hidden material rules from microscopic data to predict large-scale behavior

Researchers from the National University of Singapore (NUS) have developed artificial intelligence (AI) methods that learn the large-scale behavior of complex materials from microscopic data. By automatically identifying a small number of hidden variables that capture the collective behavior of a system, the methods can predict how materials evolve over time while reducing the need for costly simulations.
AI Generated Robotic Content

Share
Published by
AI Generated Robotic Content

Recent Posts

Choosing an LLM Inference Optimization Technique: A Decision-Tree Approach

In this article, you will learn how to match an LLM inference optimization technique to…

3 hours ago

Bring structured and unstructured data together with Amazon Quick

Organizations collect data in many forms. Structured data lives in databases and spreadsheets, whereas unstructured…

3 hours ago

How Nammu21 automates syndicated loans on Google Cloud Universal Ledger

The syndicated loan and private credit markets, both critical components of corporate finance, have long…

3 hours ago

The Lenovo IdeaPad Slim 5x Is Only $900 Right Now

Laptop prices keep going up, but this discount on the Lenovo IdeaPad Slim 5x is…

4 hours ago

Leading AI models are biased against certain accents when scoring interviews, presentations and tests

Artificial intelligence models replicate, and sometimes amplify, all sorts of human biases. In the last…

4 hours ago