It's time for a comprehensive framework on AI.
Last month, researchers at Duke University demonstrated that incorporating known physics into machine learning algorithms could result in new levels of discoveries into material properties,
by the institution. They undertook a first-of-its-kind project where they constructed a machine-learning algorithm to deduce the properties of a class of engineered materials known as metamaterials and to determine how they interact with electromagnetic fields.The results proved extraordinary. The new algorithm accurately predicted the metamaterial’s properties more efficiently than previous methods while also providing new insights.
“By incorporating known physics directly into the machine learning, the algorithm can find solutions with less training data and in less time,” said Willie Padilla, professor of electrical and computer engineering at Duke. “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.
They did that by imposing upon the neural network a physics called a Lorentz model. This is a set of equations that describe how the intrinsic properties of a material resonate with an electromagnetic field. This, however, was no easy feat to achieve.“When you make a neural network more interpretable, which is in some sense what we’ve done here, it can be more challenging to fine tune,” said Omar Khatib, a postdoctoral researcher working in Padilla’s laboratory.
more efficiently than previous neural networks the group had created for the same tasks by dramatically reducing the number of parameters needed for the model to determine the metamaterial properties. The new model could even make discoveries all on its own.“Now that we’ve demonstrated that this can be done, we want to apply this approach to systems where the physics is unknown,” Padilla said.
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