This new research may lead to new opportunities for neural-network development.
by different factors. In a similar fashion, artificial neurons attribute biases and numeric values to certain connections during the training phase.One drawback of these neural-network systems is that they don't respond well to chaos, this is also referred to asWrecking ball analogyImagine a wrecking ball swinging about. It has both kinetic and potential energy at play. If we take a picture of it mid-swing, we cannot tell for sure where the wrecking ball is headed or at what speed.
This is how conventional neural-networks analyze data. If we implement Hamiltonian mechanics into neural-networks' understanding, it can analyze the movement of the ball entirely, meaning it can look at where it was at what time and can determine"Physics-enhanced neural networks learn order and chaos" Anshul Choudhary, John F. Lindner, Elliott G. Holliday, Scott T. Miller, Sudeshna Sinha, and William L.
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