Digital twin design patterns including state-based and simulation-based modeling, real-time state synchronization, predictive maintenance via simulation, what-if scenario analysis, 3D visualization, and platform guidance for Azure Digital Twins and AWS IoT TwinMaker.
Installation
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Digital twin design patterns including state-based and simulation-based modeling, real-time state synchronization, predictive maintenance via simulation, what-if scenario analysis, 3D visualization, and platform guidance for Azure Digital Twins and AWS IoT TwinMaker.
allowed-tools
Read, Grep, Glob, Bash
Digital Twin Patterns
When to use
Designing a digital twin architecture from scratch (shadow vs twin vs simulation)
Modeling twin ontologies with DTDL, RealEstateCore, or custom schemas
Implementing device-to-twin and twin-to-device synchronization pipelines
Building predictive maintenance with RUL models and anomaly detection
Running what-if scenario analysis in a sandboxed twin environment
Selecting Azure Digital Twins, AWS IoT TwinMaker, or open-source alternatives
Core principles
Maturity determines complexity — start with a digital shadow (read-only), graduate to bidirectional twin only when control use cases are proven
Graph topology mirrors physical topology — site → building → floor → room → device; queries follow the physical hierarchy
Eventual consistency is fine for monitoring; not for control — sub-second twin updates matter only in closed-loop control scenarios
Staleness thresholds are a first-class feature — a twin that hasn't updated in 5x its expected interval is broken, not just quiet
Sandbox before touching the physical asset — all what-if scenarios run on a cloned twin state, never against the live twin
Reference Files
references/twin-modeling.md — maturity levels, state-based vs simulation-based twins, DTDL ontology design, example twin state document