Expanding the Operational Ecosystem of Space Systems Through AI-Enabled Digital Twins
Bibliographic record
Abstract
The fourth industrial revolution is underway, where machine learning and artificial intelligence (AI) technologies have made significant advancements and are changing industries. One area where AI is advancing the state-of-the-art is in generating models and making inferences on data in digital twins. Digital twins are a digital representation of a system’s architecture, design, and features throughout a system’s lifecycle. The Cognitive Mission Manager (CMM) is a program at Lockheed Martin developing a cognitive assistant for firefighter operations, which includes creating a digital twin of an entire wildland fire incident by fusing real-world data from satellites, aircraft, and ground assets into a system-of-systems and environmental digital twin in NVIDIA Omniverse. Through this digital twin, CMM is able to show predictions of fire spread and use reinforcement learning to provide recommended courses of action. With these advances in AI technologies, firefighting and other complex operational environments could be standing on the precipice of a monumental tipping point that improves the use of data from satellites and other sources. This tipping point could be similar to the revolution of modern weather forecasting that was enabled by satellite technology and computing resources. This paper will discuss these AI advancements, how they have enabled new digital-twin capabilities, and how they can assist in expanding the multi-domain operational capabilities of firefighting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".