Multi-Fidelity Transfer Learning for Defense Applications
Overview
What you need to judge this in 30 seconds
SBIR · Phase: BOTH · Topic MDA27BZ02-DV010 · Solicitation 27.BZ
Rapid Artificial Intelligence (AI) integration into the MDS is required for the United States to properly defend itself against its adversaries. The development of robust Artificial Intelligence/Machine Learning (AI/ML) agents for complex missile defense and aerospace applications is often constrained by the computational cost, limited availability, and security restrictions of high-fidelity simulation environments. Transfer learning provides a critical pathway to overcoming these barriers. By initially training agents on low-fidelity models or unclassified representations—where data is abundant and computational demands are reduced—core behaviors, strategies, and decision-making frameworks could be efficiently developed and refined. These pretrained models could then be adapted and optimized in high-fidelity and/or environments at higher classification levels, allowing the agent to leverage its foundational knowledge while rapidly acquiring the nuanced performance required for operational deployment. This staged approach significantly reduces training time, minimizes resource expenditure, and mitigates the risk of overfitting to scarce high-fidelity/classified data. Moreover, it enables iterative development cycles where low-fidelity environments serve as agile testbeds for innovation, and high-fidelity environments refine and validate for mission-readiness. Leveraging transfer learning in this manner not only accelerates capability delivery but also ensures that AI/ML systems achieve the precision, resilience, and adaptability necessary for real-world mission success.
- Category
- R&D
- Industry
- Space & Defense
- Technology
- Needs verification
- Target stage
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- Project duration
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- Estimated preparation
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Funding
Award size and how it is paid
- Award range
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- Currency
- USD
- Total programme budget
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- Support type
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- Co-funding
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- Matching fund
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- Disbursement
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- Note
- -
Eligibility
Can we actually apply?
SBIR/STTR 은 미국 중소기업만 지원할 수 있습니다(Small Business Act 법정 요건). • 계열사를 포함해 상시 종업원 500명 이하 • 미국 시민 또는 영주권자 1인 이상이 50%를 초과해 직접 소유·지배 • 미국 내 사업장을 두고 주로 미국 내에서 사업을 영위할 것 • 수행책임자(PI)의 주된 근무처가 신청 기업일 것 출처: https://www.sbir.gov/faq/eligibility-requirements
- Company age
- Needs verification
- Employees
- 0명 ~ 500명
- Revenue limits
- Needs verification
- Consortium
- Needs verification
Location Requirements
Geography and legal-entity conditions
- Primary country
- 🇺🇸 United States
- Also eligible
- None
- Foreign companies
- No
- Local entity
- Required
- Location condition
- 미국 내 사업장을 두고 미국 시민·영주권자가 50%를 초과해 소유한 중소기업만 신청할 수 있습니다(법정 요건).
Required Documents
Required/optional · issuer · difficulty · validity · cautions
Document requirements were not captured (needs verification). Check the official announcement.
Application Process
How you apply
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Evaluation
Review process and criteria
Evaluation process not captured.
Timeline
Announcement → intake → review → agreement → execution
Process steps were not captured.
Equity & Financial Terms
Equity, loans and contract conditions
- Equity required
- Needs verification
- Loan
- Non-dilutive grant
- Duplicate funding
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- IP ownership
- Needs verification
- Deliverable ownership
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- Exclusivity
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- Right of first refusal
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- Audit & settlement
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Risk Intelligence
Should we apply at all? — five dimensions plus an overall score (lower is safer)
No risk assessment yet. Re-analyse from Admin.
IP / Idea Protection
How much of your technology and idea you must disclose
No IP assessment yet.
AI Analysis
Pros · cons · difficulty · competition · attractiveness
From paperwork and review stages
Heuristic from award size and type
Amount, terms and IP combined
Weighted average of five dimensions
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- Nothing flagged
Similar Programs
Comparable by type, country and technology
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Source & Provenance
Every value carries its source URL and fetch time
- Official URL
- https://www.dodsbirsttr.mil/topics-app/
- Application URL
- https://www.dodsbirsttr.mil/topics-app/
- Last fetched
- 2026.11.06
- Human review
- Not reviewed — check the official announcement
- First seen
- 2026.11.06
- Data status
- Auto-collected, not reviewed
- AI enrichment
- Needs verification
- PRIMARY
- HTMLSBIR/STTR 자격요건 (법정)
Fetched 4h ago
- HTML발주 기관 공식 공고
Fetched 4h ago
- · 4h ago — 최초 수집 (fde2a82d)