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Digital Engineering

Sim-to-real is an organizational-learning problem

The Defense Advanced Research Projects Agency (DARPA) is seeking proposals for Transfer from Imprecise and Abstract Models to Autonomous Technologies (TIAMAT). The program challenges a common assumption: instead of building ever more detailed simulations, diverse lower-fidelity environments may help autonomous systems learn concepts that transfer more readily to unfamiliar real-world settings.

The technical hypothesis is provocative. The management lesson is familiar: a model becomes dangerous when an organization forgets which parts of reality it left out.

A digital twin can preserve operational judgment

The National Institute of Standards and Technology (NIST) is exploring how digital twins could help manufacturers detect cyberattacks. By comparing a physical process with its virtual representation, a team may recognize changes that ordinary information-technology monitoring misses: a machine behaving differently, a process drifting, or a control command producing an unexpected physical result.

The cybersecurity potential is important. So is the knowledge-management lesson. A useful digital twin does not merely mirror equipment. It preserves an organization's understanding of what normal operation means.

Simulation is becoming part of the robotics product

The robots drawing attention at this week's Consumer Electronics Show (CES) are physical machines, but much of their meaningful development is happening somewhere less visible. NVIDIA's latest Isaac Sim release expands the use of synthetic data, cloud access, and human representations for developing and testing intelligent robots before they enter a warehouse, factory, or other operating environment.

That is more than a faster way to prototype. It suggests that simulation is becoming part of the robotics product itself.

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