To efficiently compute where waves of light, sound, or earthquakes will go when scattered by irregular obstacles is useful in various fields but difficult and expensive to do, even using recent machine learning techniques. To improve the scalability and practicality of such computations, Laurynas Valantinas and Tom Vettenburg, researchers at the University of Dundee in the UK, mapped the wave equations onto the structure of a recurrent neural network. Its minimal memory requirements allowed them to scale up wave scattering calculations by two orders of magnitude or more.
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