Physics AI for Defense Programs
Verify how a system will perform in seconds, from the bid through fielded operations.
What We Deliver
A large physics model built on your data, and the factory that produces it.
Luminary builds a large physics model grounded in real physics and trained on your simulation, experimental, and operational data. The model takes a geometry and a set of operating conditions and returns a prediction of physical behavior in seconds, with no meshing and no solver setup. Any engineer on the program, or an AI agent working on their behalf, can query it.
Producing that model is a production line, and Luminary's Physics AI Factory runs it end to end. GPU-native solvers generate training data at scale, automated pipelines train the model and validate it against evolving test suites, and each version is deployed into the tools your engineers already work in. Retraining runs as new simulation and test results land, so the model tracks the program instead of aging against it.
The repetitive, high-volume runs that consume solver capacity, the aerodynamic databases and the control-law tables, move to the model. The solver stays in the loop as the verification gate, and predictions are validated against high-fidelity simulation for the intended use. The platform deploys on-premise, air-gapped, in your VPC, or in the cloud, and every model your team builds stays yours: your data, your model, your IP.
Forward deployed engineers work alongside your team to scope the use case, build the model, and integrate it into how the program already runs. A model validated across a defined design space is reused across in-scope variants and extended across programs, and operational data folded back in keeps verification running after the system is fielded.
Physics AI is the next level of complexity in AI, and Northrop Grumman is bringing this technology to our design engineers to dramatically speed up hardware development.
Han Park, VP, Artificial Intelligence Integration, Northrop Grumman Space Systems
By leveraging NVIDIA accelerated computing and our PhysicsNeMo framework, Northrop Grumman and Luminary are demonstrating how AI Physics models can redefine the pace and scale of spacecraft design.
Tim Costa, GM, Industrial and Computational Engineering, NVIDIA
You can take complete aircraft configurations and run them in a matter of minutes. It allows a level of confidence that was unprecedented before.
Gregor Veble Mikić, Chief Aerodynamicist, Joby Aviation
Challenges in Defense
Programs compete on probability of win and on schedule. Verification speed gates both.
Verification Is on the Critical Path
Nothing moves until someone proves the design performs. Geometry cleanup, meshing, and solver setup run hours to days for a single case, and the bid, the design freeze, and the fielding decision are all gated by that queue.
You Verify a Fraction of the Envelope
Each simulation is bespoke to one geometry at one condition, and every design change starts it over. Programs end up with a handful of verified points and interpolate across everything between them.
Late-Stage Cost Is Hardware, Not Compute
After detailed design the bill is test articles, rig hours, range time, and at-sea events. Margin you cannot justify analytically gets carried as extra structure and extra mass, and it ships on every unit.
Accreditation, Not Accuracy, Decides Government Use
DoDI 5000.61 requires a verification, validation, and accreditation path for a model's specific intended use. Without one, an accurate model still cannot support the decision it was built for.
The Data Cannot Leave
Classified programs cannot ship the geometry that would prove a vendor's value. Secret-level work needs cleared people and cleared spaces, which rules out shared-cloud tooling before anyone evaluates the technology.
Nothing Follows the System Into Service
Verification ends at delivery. Sensor and operational data accumulates across the fleet, and nothing folds it back into a prediction of how the system will behave as parts wear and the threat changes.
Industry Insight
Juan Alonso on what Physics AI changes for defense programs.
Luminary CTO and co-founder Juan Alonso sits down with Barry Rosenberg of Breaking Defense to discuss where Physics AI fits in defense engineering.
Why Luminary
Use Cases
Physics AI across the program lifecycle.
Real-time physics changes what is possible at every stage, from design to manufacture and operations.
Design Exploration & Optimization
Evaluate far more architectures and configuration variants before the bid goes in, so the design you propose rests on evidence across the trade space rather than on a handful of runs.
Adversary-Platform Modeling
Build threat aerodynamic databases and behavior models to shape requirements and evaluate a system against the threat it will actually face.
Aerodynamic Databases & Surrogate Models
Generate dense aerodynamic databases for flight simulation, guidance and control, loads, and stability and control, in place of a sparse simulation matrix.
Acoustic Signature Prediction & Detection
Predict radiated signature from a candidate design, and work the inverse problem of inferring a source from measured acoustics.
Test Planning & Autonomous Flight Test
Plan information-maximizing test points against a physics model that re-assimilates live test data, and cut the instrumented points needed to clear the envelope.
Accreditation Support & Uncertainty Quantification
Quantify model error and applicability bounds so a prediction can be carried through a verification, validation, and accreditation path for a specific intended use.
Operational Simulation & Control Laws
Supply control laws and engagement-outcome tables to the mission-level simulation environments where operational analysis runs.
Digital Twins & Prognostic Health Management
Fuse operational and sensor data with physics to predict degradation and plan maintenance before it becomes a readiness problem.
See It on Your Own Program.
Bring a real design or verification problem. A Luminary engineer will show you what Physics AI looks like on it, in the environment your program requires.