[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-neural-operators-bring-real-time-virtual-sensing-to-nuclear-reactors":10},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":22,"tags":24,"sources":28,"feedback":32,"feedback_at":22,"cost_usd":32,"total_tokens":32},1304,"neural-operators-bring-real-time-virtual-sensing-to-nuclear-reactors","Neural operators bring real-time virtual sensing to nuclear reactors","A new operator-based model estimates interior fluid fields from sparse boundary data with sub‑millisecond latency and less than 5% error.","- Virtual sensing now reaches the interior of safety‑critical energy hardware in real time.\n\nThe researchers present MIMONet, a neural‑operator model that maps sparse temperature and pressure readings on a system’s surface to full‑field fluid and thermal states inside. It handles heterogeneous inputs, preserves the bilinear coupling of the governing PDEs, and decodes multiple fields with a shared latent space. Tests span simple lid‑driven cavity flow, pressurised water reactor subchannels, and fully coupled heat exchangers. Across these cases MIMONet stays under 5% relative error and runs in about 0.35 ms on an NVIDIA H200, consuming 46 mJ per inference.\n\nThis matters because traditional state estimation relies on fixed equations or retraining for each geometry, which stalls at real‑time constraints and limits mesh independence. By learning the solution operator once, MIMONet offers field‑level observability without installing sensors in hostile interiors. The speed and robustness to 50% sensor noise make it a candidate for online safety monitoring in reactors and other high‑risk thermal‑fluid systems.\n\nThe claim still rests on simulation data; experimental validation will be the true test of whether virtual sensing can replace hard instrumentation.","[\"neural-operators\",\"virtual-sensing\",\"energy-systems\"]","2026-06-16T04:00:00.000Z","2026-06-17T02:57:32.914Z","2026-06-17T02:57:35.810Z","published",null,[],[25,26,27],"neural-operators","virtual-sensing","energy-systems",[29],{"name":30,"url":31},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2412.00107",0]