AI assurance & verification
nnenum
Evidence strength: Moderate How this is scored
Python 3.9+ neural-network verifier (pip install .; nnenum 1.0.1, GPL-3.0-or-later) for ReLU networks. Combines zonotopes with star sets, uses the ImageStar trick through ONNX Runtime, and ships ACAS Xu ONNX/vnnlib examples. VNN-COMP 2020: only tool to verify all ACAS-Xu benchmarks.
Certification evidence
Cited literature: 1 DO-178C / related aerospace guidance requirement — see which with Pro
Analyst assessment: 1 NIST AI RMF function or subcategory — see which with Pro
Certification relevance. peer-reviewed or agency literature maps it to DO-178C / related aerospace guidance (1 requirement).
Cited literature: nnenum: Verification of ReLU Neural Networks with Optimized Abstraction Refinement (NASA Formal Methods 2021 (NFM 2021), 2021) — a named DO-178C / related aerospace guidance requirement.
Paper or report · The quoted wording is Pro.
Listing detail and source evidence (Pro)
Included on Pro, Team and Enterprise. Public summaries, tags and official links on this page stay free.
Cited literature and analyst assessments (Pro)
Included on Pro, Team and Enterprise. Public summaries, tags and official links on this page stay free.
Procurement evidence pack (Pro) — print cited mappings and analyst notes. The print view is not on this page.
- Vendor / publisher
- Stanley Bak
- Type
- Open source
- Open source
- Open source · GPL-3.0-or-later on the official repository https://github.com/stanleybak/nnenum. Badge is used only when that repo shows an OSI-approved license.
- Official site
- https://github.com/stanleybak/nnenum
- Industries
- Frameworks named on cited source
- None tagged. The cited pages do not name a framework we cover.
- Last verified
Listing is not an endorsement, certification or recommendation. Capabilities, availability and framework support can change. Confirm with the vendor and the relevant authority. See the disclaimer.