"Teaching Physics to AI Can Allow it to Make New Discoveries All on Its Own"

Researchers at  Duke University have discovered that incorporating known physics into machine learning algorithms can help the enigmatic black boxes attain new levels of transparency and insight into the characteristics of materials.  The researchers used a sophisticated machine learning algorithm in one of the first efforts of its type to identify the characteristics of a class of engineered materials known as metamaterials and to predict how they interact with electromagnetic fields.  The researchers stated that the algorithm was essentially forced to show its work since it first had to take into account the known physical restrictions of the metamaterial.  The researchers noted that this method enabled the algorithm to predict the properties of the metamaterial with high accuracy, more quickly, and with additional insights than earlier approaches.  Willie Padilla, professor of electrical and computer engineering at Duke, stated that by incorporating known physics directly into machine learning, the algorithm can find solutions with less training data and in less time.  Padilla noted that while this study was mainly a demonstration showing that the approach could recreate known solutions, it also revealed some insights into the inner workings of non-metallic metamaterials that nobody knew before.  The results were published in the journal Advanced Optical Materials.

SciTechDaily reports: "Teaching Physics to AI Can Allow it to Make New Discoveries All on Its Own"