Digital Twin State Store

Key Functions

Data Persistence Across Operations

The State stores holds data that needs to be retained between distinct processing steps. Rather than recomputing or losing information after each operation, state stores preserve it for later retrieval or updates. This is essential in scenarios like:

• Stream processing – maintaining aggregations or windows of streaming data
• Feature engineering – storing computed features for reuse
• Model training – caching intermediate results to accelerate iterations

Supporting Stateful Computations

In distributed or streaming architectures, stateful operations require access to historical or accumulated data. For example:

• Calculating rolling averages or cumulative sums on streaming data
• Joining current events with past state (e.g., user session history)
• Maintaining counters or aggregates that update incrementally

Fault Tolerance and Recovery

The State store enables checkpointing and recovery. If a process fails, the stored state allows systems to resume from the last checkpoint rather than starting over, which is critical for long-running data pipelines.

Digital Twin State Store as the trusted memory of the system

The Digital Twin State Store is the architectural boundary between untrusted observations and trusted operational reasoning. It is not merely an ML feature store. It is the stateful memory of the cyber-physical twin, designed to store only semantically valid, traceable, and physically admissible state variables.

Each state feature should include:

• Physical semantics: variable name, unit, bounds, rate limits, dimensional consistency, and topology scope.
• Temporal semantics: timestamp, validity window, sample rate, latency tolerance, interpolation policy, and version history.
• Provenance: source sensor, model, inference function, fusion method, confidence score, and lineage.
• Topology binding: asset identifier, process zone, conduit, upstream/downstream dependencies, and control-loop membership.
• Authority constraints: which agents, applications, and control systems may read, write, propose, or promote state transitions.
• Admissibility metadata: validation status, residual score, uncertainty band, and quarantine state.

This creates a clear operating principle: agents and analytics should interact with a validated projection of reality, not directly with raw reality claims. Security is enforced at the message and state boundary rather than inside an opaque model.

Latent states

Cyberphiz's digital twins are not passive visualization tools. They are designed to active operational reasoning systems. The critical shift is from monitoring raw sensor values to maintaining a time-consistent, physics-grounded representation of the asset’s latent state: the inferred physical condition that best explains all available evidence. In a cyber-physical system, this latent state becomes the security-relevant ground truth. Sensors may lie, agents may hallucinate, controllers may be compromised, and messages may be delayed, but conservation laws, material limits, thermodynamics, electrical relationships, and causal dependencies remain harder to falsify at system level.

Latent State

A latent state is the time-consistent, physics-grounded representation of an asset’s true physical condition inferred from heterogeneous and noisy measurements; in modern digital twins it serves as the operational ground truth that explains and reconciles all available evidence. Unlike raw sensor readings, which can be delayed, corrupted, or maliciously altered, the latent state encodes the conserved quantities, material limits, thermodynamic constraints, and causal relationships that govern system behavior, allowing the twin to reason about what must be true even when individual data streams disagree. By casting the asset’s condition as a latent state within a dynamical, constrained model, the digital twin moves beyond passive visualization to active inference, prediction, and decision support—providing a security‑relevant, falsifiable basis for anomaly detection, control, and forensic analysis in cyber‑physical systems.

How latent states are calculated

Latent state calculation depends on the asset class, process physics, available observers, and required latency. Cyberphiz should classify latent states as simple or complex in the schema and bind each latent state to a defined inference function.

Latent state class	Example	Typical inference method	Use in risk monitoring
Simple derived state	Flow imbalance, average temperature, differential pressure, normalized vibration energy.	Unit conversion, deterministic formula, rolling window, sensor fusion.	Fast screening, health indicators, early residual generation.
Physics-constrained state	Pipeline pressure field, gas velocity profile, electrolyzer gas production rate, battery thermal state.	Mass and energy balances, equation of state, Faradaic law, momentum equations, heat-transfer models.	Detection of physically impossible reported conditions and unsafe process evolution.
Distributed latent field	Pressure P(x,t), temperature T(x,t), concentration C(x,t), stress σ(x,t).	PDE solvers, method of characteristics, reduced-order models, PINNs, surrogate models.	Virtual sensors, blind-spot monitoring, transient propagation analysis.
Probabilistic latent state	Posterior probability of rupture, leak, membrane perforation, thermal runaway, or unsafe command propagation.	Bayesian filters, dynamic Bayesian networks, probabilistic graphical models, particle filters.	Consequence-based prioritization and uncertainty-aware escalation.

The workflow is deliberately structured: sensor metrics update latent states; residuals are computed from latent states; posterior risk is updated from residuals and context; control decisions consume the posterior risk and admissibility status.