The field of particle physics is approaching a critical horizon defined by challenges including unprecedented data volumes and detector complexity. Upcoming ...
In biomedical modeling, the integration of mechanistic and data-driven approaches is reshaping how we interpret and predict complex biological phenomena.
2026 will be a transformative year in this area — one where force fields redefine the boundaries of atomistic simulation, making previously unthinkable modeling and discoveries routine. With workflows ...
By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...
A team of UCSF researchers successfully tested several mainstream AI agents for the ability to analyze big data on women's ...
A Rensselaer Polytechnic Institute (RPI) engineering professor, Shaowu Pan, Ph.D. and his team of students have integrated ...
The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...
Research authored by partners from the Bottle Consortium and published in Nature Communications this month aims to challenge ...
Your brain calculates complex physics every day and you don't even notice. This neuromorphic chip taps into the same idea.
Abstract: The security of Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems has been significantly enhanced through the integration of effective intrusion detection systems ...
Abstract: Quantum Machine Learning (QML) has emerged as a promising frontier within artificial intelligence, offering enhanced data-driven modeling through quantum-augmented representation, ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results