HyperPSCA: A Unified Autopoietic Hypergraph Engine for Cross-Domain Scientific Discovery, Patent Screening, and Material/Biomedical Co-Evolution

This research explores the development and implementation of HyperPSCA, an executable, autopoietic semantic hypergraph engine designed to facilitate cross-domain scientific discovery and process simulation. Operating on an NDJSON-LD data format, the computational framework integrates multi-domain knowledge representation with dynamic mathematical modeling to enable real-time system analysis. In the context of process systems and digital twin engineering, the engine utilizes a fourth-order Runge-Kutta (RK4) ordinary differential equation (ODE) solver to simulate dynamic state transitions, virtual knockouts, and metabolic or chemical pathway perturbations. By linking semantic hypergraph structures with quantitative numerical solvers, the system maps complex chemical-biological interactions and identifies potential molecular inhibitors. The framework provides a structured methodology for co-evolving material

properties and biochemical targets, offering a robust digital twin architecture for precision medicine and process control. This approach bridges the gap between qualitative semantic relations and quantitative dynamic simulations, establishing a scalable platform for predictive modeling, patent screening, and automated logical inference in complex chemical and biological networks.

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