Machine learning has brought about one of the biggest revolutions in physics. In this article, we present several machine learning examples in physics developed using Neural Designer, illustrating how predictive models can be applied to complex physical systems.

Detect gamma rays using machine learning

Neural Designer leverages machine learning to approximate the gamma function, providing insights into complex mathematical behavior and supporting computational research.
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Detect the Higgs boson using machine learning

Advance high-energy physics with Neural Designer, applying advanced machine learning to Higgs boson experiments to extract meaningful patterns, analyze complex data, and support scientific breakthroughs.
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Classify asteroid orbits using machine learning

Neural Designer leverages advanced machine learning for orbit classification, processing astronomical and celestial data to identify patterns and improve trajectory predictions with higher accuracy.
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Star types classification using machine learning

Neural Designer leverages machine learning for star type classification, processing astronomical data to improve identification accuracy and advance astrophysical studies.
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Detect gamma rays using machine learning

Learn how Neural Designer uses machine learning to analyze superconductivity, modeling materials to improve predictions, research accuracy, and understanding in physics.
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