Physics AI, Translated for Engineers Who Speak Simulation
Physics AI borrows vocabulary from simulation, machine learning, and statistics, but the terms are still forming and the overlaps are misleading.
This reference maps the core Physics AI terms to the six stages of Large Physics Model development, with each entry grounded in CFD and FEM concepts engineers already know.
When Your Model Answers With Confidence, But Shouldn’t
An LPM returns results in seconds, but a standard neural network delivers a confident answer whether it’s drawing on dense training data or guessing. Nothing in the output signals which is happening.
Luminary’s OOD detection evaluates the query before physics inference starts, flagging inputs outside the model’s training distribution. Uncertainty quantification gives predictions a margin of error that means something: “450 MPa ± 15 MPa” instead of a false point estimate. Together, they let teams sweep thousands of configurations at LPM speed and know which results to act on.
Luminary-SMART: Our New Architecture, Built to Solve Physics AI’s Most Elusive Problem
Most Physics AI models train on high-fidelity simulation data tied to detailed meshes. Those meshes are adaptively refined where physics is hardest, giving the model a shortcut to the right answer during training. At inference, the customer uploads a raw geometry file. The shortcut disappears.
Luminary-SMART reads geometry as a point cloud, not a mesh. It learns the underlying physics rather than the distribution of cells that proxies for it.
An Aerospace Company’s Journey from Simulation to Physics AI: The Bottleneck Was Never the Solver
The real bottleneck in aerospace simulation isn’t solver speed but the discrete, batch-based workflow itself. It follows one aerospace OEM that moved to a GPU-native solver and still waited weeks to build its aerodynamic database, then trained a Large Physics Model on those same simulations to answer new questions through inference instead of re-solving, holding low-single-digit error against the solver and avoiding nearly $10,000 in compute per iteration.
Understand how Physics AI turns a disposable, rebuilt-every-time deliverable into a reusable engineering asset that compounds across disciplines like structures and mission analysis.
Upcoming Webinar — Aug 31: The Physics AI Stack Required for Faster Engineering Decisions in the AI Era
Join Luminary’s Chief Product Officer, Suds Menon, and Marketing Director, Jason Lim, for a practical fireside chat on Physics AI: what it is, why it matters, and the technology and delivery stack needed to make it all work. The end goal is to help engineering teams use AI to explore designs, evaluate tradeoffs, and make faster product decisions.
The conversation will dive into the five layers of innovation of the Physics AI stack: deployment, platform, physics, physics AI, and industry solutions.
Upcoming Webinar — Sept 10: Luminary-SMART: Mesh-Independent Model Architecture for Large Physics Models
In this webinar, we will discuss Luminary-SMART, Luminary’s new architecture for Large Physics Models, and explain how it is designed to solve one of Physics AI’s hardest problems: building models that learn the underlying physics rather than the simulation mesh.
The session will cover how Luminary-SMART combines mesh independence with scalable model capacity, how it builds on lessons from other model architectures, and what its benchmark performance means for engineering teams deploying Physics AI in production.
ICYMI: Where Physics AI Delivers Value in Aerospace
Aerospace development runs from concept screening to in-service sustainment, and Physics AI relieves a different constraint at each phase.
This session maps Physics AI use cases across the full aerospace product lifecycle:
- Screening thousands of candidate designs before architecture lock-in
- Aerodynamic prediction within 1-3% of high-fidelity CFD returned in seconds
- Reducing verification burden where cost is measured in hardware and schedule
- Reusing design models as in-service digital twins
