New deep-learning framework reconstructs hourly PM2.5 chemical composition using air-quality and meteorological data ...
Neuromorphic computers modeled after the human brain can now solve the complex equations behind physics simulations — something once thought possible only with energy-hungry supercomputers. The ...
Large language models (LLMs) can suggest hypotheses, write code and draft papers, and AI agents are automating parts of the research process. Although this can accelerate science, it also makes it ...
Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...
Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the ...
NTN announced that it has integrated machine learning technology into its automated calculation system used for designing 3rd‑generation hub bearings, marking the first use of this approach in the ...
Abstract: The objective of optical super-resolution (SR) imaging is to acquire reliable sub-diffraction information on bioprocesses to facilitate scientific discovery. Structured illumination ...
Abstract: Machine learning algorithms and artificial intelligence (AI) have driven a revolution in many scientific and engineering fields. Now, they are slowly making their way into the field of ...
Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...
Plasma mirrors capable of withstanding the intensity of powerful lasers are being designed through an emerging machine learning framework. Researchers in Physics and Computer Science at the University ...
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