MSBAI

06/13/2026

No human touched a single mesh.
In this run, GURU Gen 2 generated 18 flight-vehicle geometries and ran 54 CFD simulations from Mach 0.3 to 6.8: meshing, converging, and adapting each one, autonomously -across Frontier, Aurora, and Raider at the same time, with no reserved queue slots.
This is a 60-second cut of the live demo. Ryland Adams narrates; AFRL's George Zagaris and I jump in. George's verdict in the room: "a game changer."
Full 12-minute walkthrough in the comments. 👇

06/07/2026

One to two weeks. That is what it can take an expert to turn a vehicle's CAD into a CFD-ready mesh. Last week our GURU AI agents did that setup work in minutes, live, across three of the nation's most powerful supercomputers at once.

From publicly released images of various high-speed vehicles, GURU synthesized 18 flight-vehicle geometries, built the meshes, then set up and ran 54 CFD simulations spanning Mach 0.3 to Mach 6, from sea level to 50 km, with solution-adaptive refinement locking onto shocks, vortices, and shock wave / boundary layer interaction mid-run. Watching it live, Air Force Research Laboratory - AFRL 's George Zagaris called it "a game changer" and "what every user dreams of."

This is the bottleneck the GAO has flagged across hypersonics programs: modeling and simulation setup consumes the scarcest resource these programs have, expert time. When AI agents carry the laborious setup, those experts are elevated to the work only they can do: aerodynamics, numerics, and validation against ground and flight test. And it runs at campaign scale, thousands of cases across full flight envelopes, which is what a faster national test cadence demands.

There is a second story here for the Department of Energy. A single autonomous workflow submitted these jobs live across OLCF Frontier, ALCF Aurora, and HPCMP Raider with no advance reservations. The queues decided where each job ran. That is the federated, multi-center ex*****on the Genesis Mission envisions, and it is how the nation will extract full value from the historic compute buildout now underway, from the leadership facilities to every new AI data center.

This week I had the honor of opening the visualization showcase at the OLCF User Summit, presenting in Oak Ridge National Laboratory's Everest facility, where a wall of pixels let the audience watch flow solutions converge and meshes adapt in real detail. The foundational research behind all of this, the multimodal representation training and the hierarchical agent architecture, was enabled by the ALCC program's leadership computing allocation. These results are the return on that investment, and I have been privileged to build on these systems since the Jaguar days in 2009. Thank you to the entire Oak Ridge Leadership Computing Facility team.

We want to aim this capability at real program needs. If your program is losing schedule to meshing and problem setup, I would like to hear about it.

06/07/2026

06/07/2026

It was an honor to present our agent based AI driven work in hypersonics in OLCF's EVEREST facility!

05/07/2026

Space Domain Awareness has a data problem most teams won’t say out loud:
The real data is too sparse to train AI that operators can actually trust. So we stopped trying to fix the data — and started generating our own.
I gave this talk yesterday at the 2026 Department of the Air Force Modeling, Simulation & Analytics Summit in Colorado Springs — hosted by STARCOM, SAF/SA CMSO, and NTSA — on the Virtual Range Architecture we’ve built at MSBAI, and how it maps to the Digital Space Range and NSTTC vision STARCOM laid out this week.
Three design choices that matter:
1. Generate the data you don’t have. TLE and EO inputs from the Unified Data Library are uneven and gap-ridden. We pre-train on millions of synthetic maneuver scenarios in NASA’s GMAT, then fine-tune on real ops data. The same playbook Tesla uses for crash scenarios autopilot has never seen.
2. Train at the embedding level, not the data level. Joint Embedding Predictive Architecture — newer than transformers, built for time-series — compresses inputs before learning. Less noise into the weights, more semantic structure out. We’re hitting AUC 0.98 on maneuver detection across 14,710 space objects, and 94–96% classification accuracy.
3. Wrap the learned components in symbolic logic. A deterministic rules engine on top is what makes the system auditable to a Guardian or an accreditor. The LLM-only crowd cannot do this part. It’s the difference between a confident model and a defensible decision.
Running at ~2-minute end-to-end latency on 20,000+ objects, with linear JEPA training scalability to 4,000 nodes on Argonne National Laboratory’s Aurora.
Built under a CDAO contract administered by Air Force DTO, embedded with Space Systems Command at the SDA TAP Lab, and tested across HPCMP, Aurora (ANL), and Frontier (Oak Ridge Leadership Computing Facility).

05/02/2026

Geometry synthesis
__ aerial vehicles