Topic Number: AF251-D016

Technical Abstract: Ranvier, LLC proposes to develop the Twin Neural Network (NN) Reliability Solution for predictive modeling of the B61-12 Tail Kit Assembly, leveraging its proprietary software platform, MODELIST. This initiative builds upon a successful Alzheimer’s Dementia (AD) proof-of-concept study, in which MODELIST enabled the creation of a bespoke NN model that achieved 91.8% accuracy in diagnosing AD using minimal patient data. This proof-of-concept study demonstrated MODELIST’S ability to rapidly create high-performing machine learning (ML) models, providing a strong foundation for this proposed effort.

Our primary objective is to develop a twin NN model for predictive reliability modeling, replacing outdated techniques to better assess and predict the reliability of the B61-12 Tail Kit Assembly. This model will exceed traditional approaches in accuracy, speed, ease of use, and memory efficiency by integrating real-world and counterfactual components for near-real-time reliability predictions. The validated and tested model will be delivered on a dedicated hardware platform designed for USAF operational needs. Our secondary objective focuses on further refining MODELIST for commercialization. MODELIST will undergo enhancements to its user interface (UI) and user experience (UX) to ensure it is intuitive, user-friendly, and ready for widespread adoption across industries. This effort will also address bugs, expand functionality, and finalize MODELIST’S design for USAF and commercial release. At the conclusion of the project, Ranvier, LLC will deliver a fully developed MODELIST platform to the USAF.

MODELIST offers several unique advantages over traditional modeling techniques. Its no-code interface makes it accessible to non-technical users while still enabling them to develop advanced ML models. By avoiding exhaustive trial-and-error testing, MODELIST achieves faster model training without sacrificing predictive power, significantly reducing development times and costs compared to other platforms currently on the market. Users can create, customize, and deploy a variety of model types with just a few clicks. This project addresses key challenges in predictive reliability modeling. Traditional approaches require significant expertise, high computational costs, and manual processes. The streamlined development capabilities of MODELIST will enable rapid creation and deployment of bespoke ML models.

Simultaneously, Ranvier, LLC will pursue a comprehensive commercialization strategy to transition MODELIST into broader markets, including other federal agencies and the private sector. With global demand for predictive analytics projected to grow at a compounded annual growth rate (CAGR) of 28.3% from 2025 to 2030, MODELIST’S combination of ease-of-use, customization, power, and affordability positions it as a transformative solution in the predictive analytics market.