| Abstract: |
| This decade`s `digital wild' is inundated with high rate, voluminous combinations of physics- and human-based data. The challenge is further exacerbated by rapidly accelerating autonomous architectures lacking explainability, compromised/poisoned heterogeneous data, and managing denied/degraded/intermittent/low-bandwidth environments. These and other prohibitive symptoms weaken user certainty while concurrently increasing compute complexities and reducing edge processing compatibility, resulting in failed support of (near) real-time decision speed. Addressing these challenges motivates rigorous scientific creativity though multi-disciplinary approaches, particularly leveraging the rich mathematical properties of a network's upstream data and their fused aggregates. Computational and algebraic topology implemented by topological data analysis (TDA), both classical and emerging, provide access to these properties and a path to modality/model agnostic fusion. This talk exhibits one such ongoing success, an efficient and scalable TDA Machine Learning (TDAML) algorithm and examines several case studies recently developed including applications in surveillance, time-series forecasting, data assurance, and materials/systems inspection. After a brief overview introducing TDA, an overarching dissection of the TDAML along with a survey of its initial object detection/classification studies and emerging implementation family are presented. Finally, future directions are discussed for the TDAML leveraging arbitrary physics- and human-based data analytics/fusion towards reliable and deployable situational/state/systems/material/cyber awareness. Distribution Statement A. Approved for public release: distribution is unlimited. AFRL-2026-0970. |
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