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People often ask me how RB-FEA technology relates to "Physics AI". It’s an important question for us at Akselos, since it relates to what it means to trust a fast prediction from a model deployed on critical equipment. "Physics AI" usually refers to neural-network surrogates trained on a library of full-order solves (FEA for structures, CFD for fluids), across sampled loads, geometry, materials, and boundary conditions. It's a genuinely powerful idea, and once trained, it's fast. Its limitation is in auditability and interpretability, since the connection to the original equations of physics is obscured. RB-FEA starts from the same kind of training data. But instead of fitting a mapping between inputs and outputs, it uses those FEA solutions to build a reduced basis, then solves the original governing equations with the reduced basis. The choice in RB-FEA to solve the same governing equations as FEA means every result is a direct solution to the physics, while still running at a fraction of the cost of a conventional full order solve. Several crucial properties for reliable deployments follow from this, such as: - A computable accuracy check on every result, grounded in the physics being solved, not just a confidence score - Automatic enrichment: when that check shows error above tolerance, the reduced basis updates itself in the background, no manual retraining step - System-level scale: components couple at the reduced-basis level, so full asset models can exceed 100 million FEA degrees of freedom and still run efficiently I wrote up the longer version of this comparison, with more of the math behind it, here: https://lnkd.in/gSnc88FK

Recent Verdantix research finds visibility into asset condition rarely means faster action. Hear what closes that gap live on Sep 16.

🧩The data is there. The confidence to act on it often isn't. I spent months talking with 40 decision-makers across oil & gas and chemicals for this white paper, commissioned by Akselos. Executives, engineers, the people actually signing off on capital and risk. There's more to it than what made the final pages. On Sep 16, I'm sharing it live alongside Oz R.ngside Oz R.: What surprised me most, and how it's shaping the way I think about this space. Join us: 📅 Wed, Sep 16, 2026 🕓 9:00 AM CST Register here: https://lnkd.in/grAGWWAt #IndustrialAI #Industry40 #Akselos #Verdantix #Webinar

Recent Verdantix research finds visibility into asset condition rarely means faster action. Hear what closes that gap live.

Sayanh Alam (Verdantix) and Oz Rodriguez (Akselos) walk through the findings live, and what they mean for your operations. Register now.

A new Verdantix survey finds cost, safety, and performance priorities sit with different teams. See how leaders close that gap live, Sep 16.