{"id":17175,"date":"2024-01-10T13:14:30","date_gmt":"2024-01-10T12:14:30","guid":{"rendered":"https:\/\/www.predictland.com\/casos-de-exito\/finding-discrete-symmetries-of-physical-systems-with-neural-networks\/"},"modified":"2025-02-17T16:18:22","modified_gmt":"2025-02-17T15:18:22","slug":"finding-discrete-symmetries-of-physical-systems-with-neural-networks","status":"publish","type":"caso-de-exito","link":"https:\/\/www.predictland.com\/en\/case-studies\/finding-discrete-symmetries-of-physical-systems-with-neural-networks\/","title":{"rendered":"Finding discrete symmetries of physical systems with neural networks."},"content":{"rendered":"\n
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PredictLand participates in a research with Machine Learning techniques in collaboration with a research institute of the Consejo Superior de Investigaciones Cient\u00edficas.<\/p>\n\n\n\n

Customer<\/h2>\n\n\n\n

INMA(Instituto de Nanociencia y Materiales de Arag\u00f3n<\/a>) is a joint research institute between CSIC (Consejo Superior de Investigaciones Cient\u00edficas) and Unizar (Universidad de Zaragoza). It is a world reference in areas ranging from materials science to quantum computing. <\/p>\n<\/div>\n\n\n\n

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Challenge<\/h2>\n\n

The challenge we set ourselves in the collaboration was to find the discrete symmetries of a physical system with machine learning<\/em> based techniques. Finding such symmetries is of paramount importance for two reasons: <\/p>\n\n