As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models ...
Materials with advanced customized properties drive innovation in a number of real-life applications across various fields, such as information technology, transportation, green energy and health ...
Using AI and machine learning as transformative solutions for semiconductor device modeling and parameter extraction.
More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...
Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational ...
A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational ...
Why it’s important not to over-engineer. Equipped with suitable hardware, IDEs, development tools and kits, frameworks, datasets, and open-source models, engineers can develop ML/AI-enabled, ...
A scoping review shows machine learning models may help predict response to biologic and targeted synthetic DMARDs in ...
A generalizable ML framework predicts protein interactions with ligand-stabilized gold nanoclusters, supporting faster design ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback