{"id":2216,"date":"2025-03-27T16:23:39","date_gmt":"2025-03-27T13:23:39","guid":{"rendered":"https:\/\/www.prosomoiono.gr\/products-portfolio\/altair-physicsai\/"},"modified":"2025-10-08T12:20:28","modified_gmt":"2025-10-08T09:20:28","slug":"altair-physicsai","status":"publish","type":"avada_portfolio","link":"https:\/\/www.prosomoiono.gr\/en\/products-portfolio\/altair-physicsai\/","title":{"rendered":"ALTAIR\u00ae PhysicsAI\u2122"},"content":{"rendered":"

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\nALTAIR\u00ae PhysicsAI\u2122<\/h3>\n

Altair\u00ae PhysicsAI\u2122<\/strong> delivers fast physics predictions by learning from your historical data without the limitations of parametric studies. Accessible through Altair\u00ae HyperWorks\u00ae, the design and simulation platform, this AI-powered CAE technology trains models using any existing simulation studies, including those from previous design ideas, similar parts, or different programs. With modern, geometric deep learning capability, Altair\u00ae PhysicsAI\u2122 identifies the relationship between shape and performance for any physics. Once trained, PhysicsAI models can deliver predictions up to 1000 times faster than traditional solver simulations, allowing teams to evaluate more concepts and make better design decisions. <\/p>\n

Read more<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Altair\u00ae PhysicsAI\u2122 delivers fast physics predictions by learning from your historical data without the limitations of parametric studies.<\/p>\n","protected":false},"author":1,"featured_media":2217,"menu_order":18,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"footnotes":""},"portfolio_category":[47,49],"portfolio_skills":[],"portfolio_tags":[],"class_list":["post-2216","avada_portfolio","type-avada_portfolio","status-publish","format-standard","has-post-thumbnail","hentry","portfolio_category-applications-en","portfolio_category-data-analytics-ai-for-engineers-en"],"_links":{"self":[{"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/avada_portfolio\/2216","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/avada_portfolio"}],"about":[{"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/types\/avada_portfolio"}],"author":[{"embeddable":true,"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/comments?post=2216"}],"version-history":[{"count":1,"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/avada_portfolio\/2216\/revisions"}],"predecessor-version":[{"id":2218,"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/avada_portfolio\/2216\/revisions\/2218"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/media\/2217"}],"wp:attachment":[{"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/media?parent=2216"}],"wp:term":[{"taxonomy":"portfolio_category","embeddable":true,"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/portfolio_category?post=2216"},{"taxonomy":"portfolio_skills","embeddable":true,"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/portfolio_skills?post=2216"},{"taxonomy":"portfolio_tags","embeddable":true,"href":"https:\/\/www.prosomoiono.gr\/en\/wp-json\/wp\/v2\/portfolio_tags?post=2216"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}