How a continuous learning cycle will redefine national advantage

When we talk about competition in the artificial intelligence era, it is easy to focus on algorithms, computing power or autonomous systems.

Those capabilities matter, but technology alone does not create national advantage. Advantage belongs to the nation that can turn promising technology into trusted capability and continuously improve it as missions and threats change.

That requires government, industry and academia to connect AI and digital engineering with physical testing (i.e. hardware-in-the-loop (HIL), flight test, etc.) and human expertise. When those elements work as one system, every model informs a test, every test produces better data, and every insight improves the next design iterations. The result is a national capacity to move systems from the laboratory into operators’ hands more quickly.

The Evolution of Aerospace & Defense Engineering

North Wind carries forward a technical lineage that reaches back through its predecessors, most recently Calspan, to the 1950s and 60s when engineering teams at our Plymouth, Minnesota, site developed regenerative storage heater technology and helped create one of the first test facility capable of real-gas, real-time hypersonic flight simulation.

It’s exciting to think that the engineering expertise we’ve built over eight decades has informed many of the nation’s major aerospace and defense milestones; from Apollo 11, which demonstrated the power of large-scale systems engineering, to the first integrated air-breathing hypersonic flight test in the 2000s and advances in hypersonic propulsion, materials and high-speed testing in the 2010s.

Fast forward to 2020 through today, AI and autonomy are enabling faster, more adaptive defense capabilities.

Today, we must evolve long-standing engineering expertise to help shape the next generation of capabilities and the next decade’s major milestones such as NASA’s new Flight Dynamics Research Facility (FDRF).

AI offers today’s technology development teams a powerful new set of tools. Test results that once took weeks to evaluate can be analyzed in hours, while simulations allow engineers to explore thousands of design variations before hardware is manufactured.

Machine learning can identify relationships across data sets rapidly, a process traditionally challenging and time-consuming for human engineers.

Combined with additive manufacturing, these capabilities can be integrated to create a more dynamic workflow rooted in continuous learning. Engineers can evaluate more options, test more hypotheses and spend less time searching for answers and more time solving meaningful problems.

Why Physical Testing Matters More Than Ever

Experience over decades of building and operating test systems has taught us that every model eventually has to meet the physical world. Today, as engineering becomes more digital, the benefits of human in the loop and flight testing increases in value across the development lifecycle.

Digital twins, simulations and AI-generated models can reduce uncertainty, but their assumptions still must be tested against reality. The more sophisticated our tools become, the more confidence we need in the data used to train, calibrate and verify them.

Hypersonic development is a good example. AI and digital engineering are allowing aerospace teams to iterate aerodynamic concepts in days rather than months, yet every promising design eventually reaches the point where it must be validated in specialized ground-test facilities capable of replicating hypersonic conditions.

Facilities for independent testing facility are now under construction and will help meet the nation’s growing need for modern ground-test capacity and capability.

Connecting Digital Engineering and Physical Testing

In the AI era, testing cannot remain the final checkpoint. It must become a continuous source of knowledge that improves models, informs design decisions and strengthens confidence in the delivered capability.

Every wind tunnel campaign, laboratory experiment and flight test should feed the engineering cycle, because rigorous testing that includes assessments of manufacturing and producibility helps innovation move faster by replacing uncertainty with evidence.

Tomorrow’s advantage will come from integrating both digital engineering and physical testing with human expertise, creating a system in which every experiment improves the next design and every design helps us plan a better experiment.

This continuous learning environment turns information into practical insights across concept development, system design, fabrication, testing and delivery, helping reduce risk and cost while strengthening national advantage.

Building a Connected Innovation Ecosystem

AI is also changing how the defense innovation ecosystem needs to work. Today's landscape is increasingly interconnected with government laboratories, universities, technology companies, startups, specialized manufacturers and defense organizations contributing distinct expertise.

Research facilities, test ranges and advanced laboratories are strategic national resources that connect people, technologies and ideas. With timely access to these facilities, digital engineering, advanced manufacturing, AI and physical experimentation can reinforce one another.

Advanced digital technologies may accelerate engineering, but speed alone is not enough. The advantage is our ability to learn, adapt and deliver trusted capabilities before the need becomes urgent.

That continuous learning cycle will be America’s next national advantage. America already has the people, infrastructure and partnerships to lead. Government and industry must now effectively connect AI-enabled digital engineering with physical testing and practical engineering experience.

Artie Mabbett is the CEO of North Wind.