DOI: 10.5281/zenodo.18653991
title: Operationally Calibrated Digital Twin for Generative-Statistical Simulation and Counterfactual Analysis of SIR

description: This repository contains an operationally calibrated digital twin generated through a structured generative-statistical pipeline integrating information geometry, statistical physics principles, and computational modeling. The digital twin is designed to replicate the structural, temporal, and probabilistic dynamics of complex interaction networks under identifiable and auditable conditions.

The system separates three formally distinct layers:





Observation Model (Sensor Layer) – Defines the measurable statistical observables and sufficient statistics extracted from empirical data.




Generative Digital Twin Layer – Implements a probabilistic generative mechanism constrained by calibrated priors and boundary conditions.




Autonomic Control Layer – Enables scenario testing, perturbation injection, and controlled counterfactual evaluation without violating identifiability constraints.



This separation prevents methodological circularity and guarantees traceability between theoretical assumptions, computational artifacts, and empirical validation metrics.



Scientific Utility

The digital twin enables:





Structural fidelity testing between real and synthetic interaction graphs.




Counterfactual experimentation under controlled boundary forcing.




Transition dynamics analysis using calibrated stochastic kernels.




Evaluation of network connectivity regimes (e.g., giant component formation).




Sensitivity analysis under parameter perturbations.




Reproducible scenario generation for policy or decision simulation.



The model is particularly suited for:





Complex systems research.




Statistical mechanics-inspired modeling of interaction flows.




Process mining and resource transition modeling.




Probabilistic network reconstruction.




Synthetic data generation under identifiable constraints.





Generation Pipeline

The digital twin is generated through the following reproducible stages:





Data Ingestion and Normalization





Temporal alignment.




Resource/state extraction.




Observable definition.






Statistical Sensor Calibration





Estimation of empirical distributions.




Transition probability inference.




Network topology measurement.




Higher-order structural metrics computation.






Prior Construction





Maximum entropy–consistent priors.




Information-geometric parameterization.




Constraint imposition based on empirical observables.






Generative Simulation





Stochastic process realization.




Boundary forcing via observed exogenous arrivals (if configured).




Resource transition propagation through probabilistic kernels.






Holdout Validation





Out-of-sample structural comparison.




Connectivity metrics.




Distributional divergence tests.




Temporal dynamic consistency checks.





All steps are deterministic under fixed seeds and configuration files, ensuring full reproducibility.



Calibration Methodology

Calibration is performed using:





Empirical transition matrix estimation.




Distributional alignment (marginal and joint).




Structural graph metrics (giant component size, degree distributions, clustering).




Temporal consistency metrics.




Constraint-based reweighting when necessary.



The model enforces identifiability by:





Avoiding hard-coded state transitions.




Separating observed forcing from generative internal dynamics.




Explicitly documenting priors and posterior adjustments.




Maintaining full traceability of parameter updates.





Counterfactual and Interventional Capability

The digital twin supports:





Perturbation of arrival processes.




Controlled alteration of transition probabilities.




Resource allocation stress testing.




Policy intervention simulations.



Counterfactual validity is constrained by:





Identifiable parameter domains.




Structural consistency checks.




Information-theoretic divergence thresholds.





Reproducibility

Reproducibility is ensured through:





Versioned configuration files.




Seed control.




Serialized priors.




Explicit data transformation logs.




Modular pipeline execution.



All experimental runs are auditable and re-executable under identical computational environments.



Intended Research Applications





Digital twin validation studies.




Interaction network topology research.




Stochastic process modeling.




Synthetic data benchmarking.




Policy stress-testing simulations.




Methodological research in identifiable generative modeling.