on the NVIDIA website<\/mark><\/a>, Apollo has been proving its worth in practical applications for quite some time now.<\/p>Applied Materials, one of the world’s largest chip-making equipment manufacturers, has used Apollo to boost their semiconductor process simulations by a whopping 35 times. This turned calculations that would take hours into results available in just a few seconds, drastically shortening both the R&D process and production control.<\/p>
Cadence, a global leader in software for designing integrated circuits and CFD simulations, has built a digital twin of an airplane on Apollo that operates in real-time. This allows the aerodynamic model to instantly respond to configuration changes, making it interactive aerodynamics rather than a classic simulation.<\/p>
Synopsys, a key provider of EDA (Electronic Design Automation) tools, has achieved up to a 500-fold acceleration in computational fluid dynamics, cutting down simulations from all night to just one quick iteration.<\/p>
Meanwhile, Northrop Grumman (a defense industry giant) and Luminary Cloud (a cloud platform specializing in CFD simulations) are using Apollo to design rocket engines in just a few seconds, which allows them to test thousands of design variations and achieve optimizations that were practically unattainable before.<\/p>
Still fallible<\/h4>
Let’s be clear about this: Apollo doesn’t get physics the way humans do. It doesn’t know equations. It doesn’t grasp concepts like energy, mass or fields. It just learns from data \u2014 and it’s only as good as the data we feed it. That means it can’t replace full simulations in critical applications, it might make mistakes if it encounters situations different from those it was trained on, and it requires really high-quality training data.<\/p>
That’s why companies shouldn’t treat the results of this tool as the “ultimate truth”. Apollo excels at rapid prototyping, but the final decision always lies with traditional physical simulations.<\/p>
An additional issue is that Apollo requires generating a ton of data beforehand, and that means relying on traditional, slow simulations often run on expensive computing clusters. Large companies have these resources, so (again) they’ll benefit first.<\/p>
So, Apollo isn’t a tool that solves every physics problem. But it totally changes the pace at which engineers work. And in technology used in business, time is everything \u2014 it impacts innovation, costs and market position.<\/p>","protected":false},"excerpt":{"rendered":"
NVIDIA is rolling out Apollo \u2014 AI models that can predict complex physical phenomena in seconds instead of hours. It promises a huge speed boost for engineers, though the technology still has its limitations.<\/p>\n","protected":false},"author":465,"featured_media":16187,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"rank_math_lock_modified_date":false,"footnotes":""},"categories":[803],"tags":[],"popular":[],"difficulty-level":[],"ppma_author":[892],"class_list":["post-16250","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-it-and-technology"],"acf":[],"authors":[{"term_id":892,"user_id":465,"is_guest":0,"slug":"kmironczuk","display_name":"Krzysztof Miro\u0144czuk","avatar_url":{"url":"https:\/\/haimagazine.com\/wp-content\/uploads\/2025\/10\/awatar-2.png","url2x":"https:\/\/haimagazine.com\/wp-content\/uploads\/2025\/10\/awatar-2.png"},"first_name":"Krzysztof","last_name":"Miro\u0144czuk","user_url":"","job_title":"","description":"Od lat zajmuj\u0119 si\u0119 nowymi technologiami w biznesie, edukacji i codziennym \u017cyciu. W centrum mojej uwagi pozostaje cz\u0142owiek \u2013 i to, by technologia wyr\u00f3wnywa\u0142a szanse, zamiast tworzy\u0107 bariery."}],"_links":{"self":[{"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/posts\/16250","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/users\/465"}],"replies":[{"embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/comments?post=16250"}],"version-history":[{"count":1,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/posts\/16250\/revisions"}],"predecessor-version":[{"id":16251,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/posts\/16250\/revisions\/16251"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/media\/16187"}],"wp:attachment":[{"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/media?parent=16250"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/categories?post=16250"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/tags?post=16250"},{"taxonomy":"popular","embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/popular?post=16250"},{"taxonomy":"difficulty-level","embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/difficulty-level?post=16250"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/ppma_author?post=16250"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}