Verifiable Success in handling Unknown Unknowns in Space Habitat Simulations and a Cyber-Physical System

We propose a layered approach which is, adaptive to different levels of computational resources: 1. Formal Methods – Prof. Kristin Yvonne Rozier expands upon NASA’s R2U2 framework, integrating proactive strategies for handling “unknown unknowns” by embedding learning and exploration directly into formal verification methods ensuing compliance with NASA’s verifiability requirements. 2. Physics-Based Bayesian reasoning – Prof. Ilias Bilionis takes the next step with the goal to improve resilience and computational efficiency, strategically targeting challenges presented by unforeseen events (“unknown unknowns”). The models are currently one of the practical approaches for handling a dynamic environment with unforeseeable changes. enabling dynamic adaptation autonomous space habitat operations. 3. Large Language Model (LLM)-Driven Deep Reasoning – Hedinn Steingrimsson and his team tailor cutting-edge deep reasoning architectures utilizing LLMs to bolster system resilience, especially for root-cause analyses involving unknown-unknown scenarios. In close collaboration with Prof. Rozier, we leverage the strength of recent advances in neural reasoning while integrating formal verification constraints and where formal models provide safety guarantees. 1. Cyber-Physical Space Habitat Integration – Prof. Shirley Dyke applies and adapts our multidisciplinary and multimodal approach. We rigorously evaluate the practical applicability of our models in real world space habitat. With realistic experimental conditions we to rigorously evaluate our models for both fully autonomous and also crew on board scenarios. Funding directly supports theoretical advancements, experimental validations aimed at fundamentally improving “unknown unknowns” capabilities in a manner that can be fully integrated with NASA’s current operational systems. With subsequent domain adaptation valuable assets can be created for performance in dynamic operational environments across different domains.