NAVSEA Open Topic for Model-Based Systems Engineering (MBSE) Acceleration via Artificial Intelligence or Machine Learning Data Ingestion
AI Overview
NAVSEA seeks AI/ML technology to automatically convert legacy AEGIS Combat System documentation into Systems Modeling Language models, eliminating manual data entry and enabling rapid cyber assessment and system design updates without diverting capability development funding.
This summary is AI-generated from the official solicitation.
Key Details
Official Description
The Navy’s AEGIS Combat System was initially developed in the late 1970s and continues in use today, with updates and changes to the core 1970s design. As a result of this multi-year development, much of its system design documentation is only available on paper or in disparate legacy digital formats that are not easily converted to models. Given the continuous development nature of weapons systems, a shift in program focus to modeling would necessarily divert funding away from capability develo...
Change History
NAVSEA Open Topic for Model-Based Systems Engineering (MBSE) Acceleration via Artificial Intelligence or Machine Learning Data Ingestion
Status changed from Pre-Release to Open
NAVSEA Open Topic for Model-Based Systems Engineering (MBSE) Acceleration via Artificial Intelligence or Machine Learning Data Ingestion
# Q&A Changes Summary **Structure Change:** Q&A was reorganized and consolidated. Original Q1–Q3 were replaced with numbered Q1–Q11, with significant consolidation and duplication. **Key Additions:** - Q1: Specific deliverable format requirements (SysML version, Cameo version, model exchange format) - Q2: Government dataset availability for Phase I - Q3–Q4: Merged duplicate questions on model scope, accuracy metrics, automation level, government data, and Cameo/SysML versions - Q5: AI environment deployment (on-premises vs. approved external service) - Q6: Emphasis on hardest ingestion challenges (cable sheets, network config data) - Q7: SysML v1 vs. v2 transition timeline alignment with Feb 2026 OUSW guidance - Q8: Specific tool versions (CATIA Magic/Cameo Systems Modeler version, Teamwork Cloud usage) - Q9–Q11: Reposted original Q1–Q3 verbatim **Critical Clarifications Pending:** No answers provided to new or reposted questions; all remain unanswered. Original response-pending status appears unchanged. **Notable Pattern:** Significant question duplication suggests possible administrative consolidation effort without government responses yet issued.
NAVSEA Open Topic for Model-Based Systems Engineering (MBSE) Acceleration via Artificial Intelligence or Machine Learning Data Ingestion
Added new Q1 with 3 questions on AI model usage (commercial vs. Navy-approved), Phase II deployment environment (on-site vs. external Government service), and Navy test data provision. Retained all 5 questions from original Q2/Q3 covering Cameo integration architecture, SysML version targeting, synthetic data acceptability, model accuracy metrics, and human involvement clarification.
NAVSEA Open Topic for Model-Based Systems Engineering (MBSE) Acceleration via Artificial Intelligence or Machine Learning Data Ingestion
The original Q1 was expanded into 5 sub-questions clarifying: (1) flexibility in Cameo integration architecture, (2) required SysML/Cameo versions, (3) use of synthetic data for Phase I, (4) model validation metrics, and (5) reconciliation between "no human input" automation vs. post-generation modifications.
NAVSEA Open Topic for Model-Based Systems Engineering (MBSE) Acceleration via Artificial Intelligence or Machine Learning Data Ingestion
This Q&A clarifies that applicants need to confirm whether their conversion solution must operate natively within Cameo MagicDraw or can use external tools for model generation with subsequent import, and should identify specific tool/SysML version compatibility requirements to propose against.
NAVSEA Open Topic for Model-Based Systems Engineering (MBSE) Acceleration via Artificial Intelligence or Machine Learning Data Ingestion
New opportunity: NAVSEA Open Topic for Model-Based Systems Engineering (MBSE) Acceleration via Artificial Intelligence or Machine Learning Data Ingestion
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