This NSF project aims to transform the way Artificial Intelligence (AI) transfers learned knowledge from simulated environments to real-world applications. The project will bring transformative change to AI by integrating symbolic reasoning with advanced hyperdimensional computing—a brain-inspired approach—thus enabling transparent and efficient interpretation of complex data under conditions where traditional deep learning struggles. This will be achieved by developing a novel neuro-symbolic framework that facilitates rapid, data-efficient knowledge transfer while maintaining interpretability across diverse cyber-physical systems. The intellectual merits of the project include its innovative use of hyperdimensional mathematics and advanced data transfer algorithms to overcome data and performance limitations, setting new benchmarks in adaptive learning. The broader impacts of the project include significant advancements in autonomous systems, robotics, and cybersecurity, as well as enhanced educational initiatives, increased opportunities for STEM, and strengthened industry collaborations. The project develops a novel neuro-symbolic framework that combines advanced hyperdimensional mathematics with deterministic finite automata (DFA) and knowledge graphs to create robust, interpretable models for knowledge transfer. By encoding high-dimensional symbolic representations, the approach enables efficient abstraction and communication of complex patterns across diverse domains. A key innovation lies in the implementation of a data-driven DFA mechanism that systematically extracts, organizes, and transfers semantic structures, ensuring reliable cross-platform data transfer and seamless adaptation between simulated and real-world environments. In addition, the framework incorporates algorithmic enhancements that facilitate dynamic data fusion and efficient pattern matching across heterogeneous datasets, further boosting its adaptability. This technical foundation is designed to reduce data requirements by more than 100 times and compress transfer learning timelines from days to minutes in simulation-to-simulation scenarios and from days to hours in simulation-to-real applications, thereby setting a new benchmark for speed, reliability, and scalability in adaptive cyber-physical systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.