PRA, regulation, and protection system risk quantification: A DES/MPP refinement for risk-informed, performance-based oversight

We propose and demonstrate an alternative quantification backbone based on Marked Point Process (MPP) implemented via Discrete Event Simulation (DES) (MPP/DES). The platform renders the probability law explicit at the level of state-/time-dependent intensities and marks, represents maintenance/test resets and environment coupling, and yields pathwise metrics aligned with inspection and enforcement (e.g., time-to-accident distributions, Key Performance Indicators (KPIs) exceedances, downtime). The framework preserves traditional Probabilistic Risk Assessment (PRA) logic while avoiding fragile calibrations and clarifying the separation of aleatory vs. epistemic uncertainty. A proof-of-concept illustrates how MPP/DES outputs map to regulatory safety margins and performance thresholds, supporting risk-informed, performance-based oversight (e.g., under the Accelerating Deployment of Versatile Advanced Nuclear for Clean Energy Act of 2024 (ADVANCE)). Dynamic Probabilistic Risk Assessment (DPRA) artifacts (e.g., Dynamic Event Tree (DET) branches, solver coupling) can be layered where nonlinear plant physics dominate.