Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...
The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...
Cardiovascular disease is the leading cause of death worldwide. Hypertension, as one of the predisposing factors of cardiovascular disease, is an important reason for the high incidence of ...
A physics informed machine learning model predicts thermal conductivity from infrared images in milliseconds, enabling fast, ...
The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research such as drug design and energy storage. However, the lack of a ...
In this special guest feature, Ori Geva, Co-Founder and CEO of Medial EarlySign, discusses how the ue of machine learning can help create new opportunities for earlier intervention and delivery of ...
Recent advancements in machine learning have significantly impacted the domain of high-speed electronic systems. By leveraging state‐of‐the‐art algorithms and novel network architectures, researchers ...
The growing potential of artificial intelligence (AI) and machine learning (ML) in embedded systems is driving new application solutions and products, but developing AI-based systems can be ...
Keysight Technologies has introduced a new Machine Learning Toolkit as part of its latest Device Modelling Software Suite, aiming to reduce the time required for semiconductor device modelling and ...
What is a dynamically reconfigurable processor (DRP)? How DRPs accelerate machine-learning applications. Why is the RZ/V2H well-suited for robotic apps? Renesas's RZ/V2H system-on-chip (SoC) is the ...
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