{"id":215741,"date":"2025-06-11T06:16:26","date_gmt":"2025-06-11T11:16:26","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2025\/06\/optical-neural-engine-can-solve-partial-differential-equations"},"modified":"2025-06-11T06:16:26","modified_gmt":"2025-06-11T11:16:26","slug":"optical-neural-engine-can-solve-partial-differential-equations","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2025\/06\/optical-neural-engine-can-solve-partial-differential-equations","title":{"rendered":"\u2018Optical neural engine\u2019 can solve partial differential equations"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/optical-neural-engine-can-solve-partial-differential-equations.jpg\"><\/a><\/p>\n<p>Partial differential equations (PDEs) are a class of mathematical problems that represent the interplay of multiple variables, and therefore have predictive power when it comes to complex physical systems. Solving these equations is a perpetual challenge, however, and current computational techniques for doing so are time-consuming and expensive.<\/p>\n<p>Now, research from the University of Utah\u2019s John and Marcia Price College of Engineering is showing a way to speed up this process: encoding those equations in light and feeding them into their newly designed \u201coptical neural engine,\u201d or ONE.<\/p>\n<p>The researchers\u2019 ONE combines diffractive optical neural networks and optical matrix multipliers. Rather than representing PDEs digitally, the researchers represented them optically, with variables represented by the various properties of a light wave, such as its intensity and phase. As a wave passes through the ONE\u2019s series of optical components, those properties gradually shift and change, until they ultimately represent the solution to the given PDE.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Partial differential equations (PDEs) are a class of mathematical problems that represent the interplay of multiple variables, and therefore have predictive power when it comes to complex physical systems. Solving these equations is a perpetual challenge, however, and current computational techniques for doing so are time-consuming and expensive. Now, research from the University of Utah\u2019s [\u2026]<\/p>\n","protected":false},"author":427,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[41,2229,6],"tags":[],"class_list":["post-215741","post","type-post","status-publish","format-standard","hentry","category-information-science","category-mathematics","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/215741","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/427"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=215741"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/215741\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=215741"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=215741"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=215741"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}