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<\/a><\/p>\n\u201cIf we want to work with quantum field theories on a computer, we have to discretize them. That\u2019s actually nothing unusual,\u201d says David M\u00fcller from the Institute for Theoretical Physics at TU Wien. Every image on a computer screen consists of small, discrete pixels; when calculating the trajectory of a lunar rocket, the calculation is performed in small, discrete time steps.<\/p>\n
It\u2019s the same in particle physics: A four-dimensional lattice is created, with three spatial dimensions and one time dimension. Each lattice point is stored on the computer, and the quantum field theory dictates how the lattice points influence each other. In this way, it is possible to simulate, for example, what happens during massive particle collisions at CERN, or how matter behaved shortly after the Big Bang. In quantum field theory, space and time are continuous. When mapping the theories onto a discrete lattice, however, one has certain degrees of freedom: Different lattice theories correspond to the same continuous theory. One must select a variant that promises the greatest computational success. If this is not done, the computer simulation may run into a dead end and fail to find the correct solution within a realistic timeframe.<\/p>\n
Different scale, same result<\/p>\n
An important key to success are so-called fixed-point equations. \u201cThere are certain formulations of quantum field theory on a lattice that have a particularly nice property,\u201d explains Urs Wenger from the University of Bern. \u201cThey ensure that certain properties remain the same, even if we make the lattice coarser or finer. If this is the case, we know: This property is reliable, it also agrees at coarse resolution \u2013 i.e., on a wide-mesh grid \u2013 with the continuum that would correspond to an infinitely fine grid.\u201d<\/p>\n
It\u2019s a bit like a map that exists at different scales: Not all details will be the same on every version of the map. But some things don\u2019t change when the scale changes \u2013 for example, which country borders which other country. This means that one can be quite sure that this property, if it is independent of the map scale, is also a property of reality itself.<\/p>\n
The success of AI<\/p>\n
Even 30 years ago, experiments were conducted to adapt the lattice formulas in this way. However, there are hundreds of thousands of parameters \u2013 far too many for a human. \u201cMany people began exploring these concepts three decades ago, but back then, we simply didn\u2019t have the technical means,\u201d says Kieran Holland from the University of the Pacific. \u201cBy joining forces with the team at TU Wien, we were finally able to revisit these long-standing ideas.\u201d<\/p>\n
To turn this vision into reality, the team has now developed a very special neural network specifically for this purpose. Ready-made AI solutions do not lead to the goal; it was necessary to develop artificial intelligence that, from the outset, guarantees compliance with the physical laws that are specified.<\/p>\n
The team has now succeeded in doing this. The result of the work: The action \u2013 the crucial physical quantity in such quantum field theories, also known from Planck\u2019s \u2018quantum of action\u2019\u2013 could be parameterized on a lattice using AI in such a way that even coarse lattices yield remarkably small errors. \u201cWe were able to show that this approach opens up a completely new way to simulate complex quantum field theories with manageable computational effort,\u201d says Andreas Ipp from TU Wien.<\/p>\n","protected":false},"excerpt":{"rendered":"Insider Brief Researchers showed that AI-designed lattice formulations can dramatically improve the accuracy and efficiency of quantum field…\n","protected":false},"author":2,"featured_media":266518,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[218,61,60,131110,82,65555],"class_list":["post-266517","post","type-post","status-publish","format-standard","has-post-thumbnail","category-science","tag-artificial-intelligence","tag-ie","tag-ireland","tag-quantum-field","tag-science","tag-tu-wien"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/266517","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/comments?post=266517"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/266517\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media\/266518"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media?parent=266517"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/categories?post=266517"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/tags?post=266517"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}