Are we just complicating the world for no reason?
The program required a lot of computational power to understand the complexity of the Hubbard model. It ran for weeks, but its final output summarized the Hubbard model in just four equations.. "When we saw the result, we said, 'Wow, this is more than what we expected.' We were really able to capture the relevant physics."
Now that the program has been trained to look for such patterns, it can be adapted to look at other similar problems without having to start from scratch. If the program can be scaled for other problems, scientists are keen to use it to design materials that offer superconductivity - where electrons flow through a material without any resistance.
Additionally, Di Sante and his colleagues are now investigating how machine learning worked in this instance to provide insights into how it works and what physicists have missed out on.We perform a data-driven dimensionality reduction of the scale-dependent four-point vertex function characterizing the functional renormalization group flow for the widely studied two-dimensional t−t′ Hubbard model on the square lattice.
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