Modular Digital Twin Architecture

• Modular digital twins are virtual representations composed of well-defined, interchangeable modules that mirror the dynamics and structure of real-world systems.
• They enable plug-and-play expansion, dynamic reconfiguration, and scalable integration across domains like urban planning, smart grids, and patient care.
• Standardized APIs and communication protocols facilitate real-time simulation, calibration, and adaptive control in complex, cyber-physical environments.

A modular digital twin is a virtual system architecture that captures the dynamics, structure, and context of a real-world entity by means of interchangeable, well-defined modules. Each module operates as an encapsulated computational or data-driven process—often aligned with a physical subsystem, a domain model, or a context resource. Modularity supports plug-and-play expansion, federated integration, upgrade without system-wide refactoring, and optimal separation of concerns. Recent research demonstrates modular digital twins in domains ranging from cyber-physical nuclear testbeds (Lim et al., 8 Jul 2025), urban scene synthesis (Tsampras et al., 15 Sep 2025), smart grids (Sen et al., 6 Dec 2024), bioprocessing (Cheng et al., 7 May 2024), robotic teleoperation (Audonnet et al., 2023), and patient care (Nitschke et al., 2 May 2025).

1. Architectural Foundations and Modularity Principles

Modular digital twins organize their software and data assets into discrete, domain-aligned components or modules. Each module is defined by its interface (ports, APIs), internal state space, configuration parameters, and transition/output functions; formally, as a tuple $\mathcal{M} = (C, S, I, O, \tau, \gamma)$, where configuration $C$, state $S$, inputs $I$, outputs $O$, transition $\tau$, and output $\gamma$ map directly to the twin’s inter-module composition (Bolender et al., 2021).

Module composition is managed via clearly typed ports and a composition operator ($\oplus$), enabling associative and commutative subsystem integration as long as interface compatibility is preserved. This property supports direct system extension—modules can be independently added, replaced, or reconfigured, provided their interfaces conform to the digital twin’s schema. Well-formedness is guaranteed when, for disjoint modules, $$(\mathcal{M}_1 \oplus \mathcal{M}_2) \oplus \mathcal{M}_3 = \mathcal{M}_1 \oplus (\mathcal{M}_2 \oplus \mathcal{M}_3)$$.

Architectures adopting strict modularity (as in NGSI-LD entity graphs for urban digital twins (Tsampras et al., 15 Sep 2025) or multi-agent orchestration for industrial twins (Dittler et al., 2022)) preserve loose coupling, support runtime dynamic reconfiguration, and streamline development cycles. The “Views and Beyond” TwinArch reference divides the architecture into orthogonal views—logical module relationships, runnable component structures, traceability matrices, and behavioral dynamics—to ensure end-to-end mapping between abstract domain entities and concrete software components (Somma et al., 10 Apr 2025).

2. Module Interaction, Interfaces, and Ontology

Modules in a digital twin communicate through well-specified interface protocols. In the urban context (Digital Twin Descriptor Service), each asset is referenced by abstract descriptors (URLs for geometry, NGSI-LD IDs for context) rather than embedded data, enabling scene graph traversal, asset federation, and context-to-representation mapping without coordination bottlenecks (Tsampras et al., 15 Sep 2025). For clinical or patient-centric systems, knowledge-graph–based modularity allows base models, ensemble fusion modules, and provenance chains to be instantiated as nodes and edges in a directed bipartite graph, facilitating dynamic model integration and traceability of predictions (Nitschke et al., 2 May 2025).

Typical interface mechanisms include:

• OPC-UA, MQTT, REST, or gRPC APIs for physical-to-digital and digital-to-physical connectivity.
• Standard message schemas (e.g., FHIR®/JSON/RDF, NGSI-LD/JSON-LD, ROS 2 messages) for decoupled multi-domain data interchange.
• Subscription/notification patterns for runtime synchronization.
• Extensible ontologies realized via JSON-LD ADEs (application domain extensions), permitting augmentation of supported entity types and relationships without breaking legacy or concurrent consumers (Tsampras et al., 15 Sep 2025).

3. Composition, Extension, and Reuse Patterns

Modular digital twins emphasize composition and adaptability via plug-and-play, factory and adapter patterns, and shared-data-repository styles. For plug-and-play composition, modules are constructed to allow runtime instantiation—new assets, simulation engines, controllers, or adapters are registered by entity or contract reference rather than tightly coupled instantiation. Example patterns (Somma et al., 10 Apr 2025):

Pattern	Function	Practical Mechanism
Adapter	Insulate protocol heterogeneity	P2D/D2PAdapter in TwinArch; MQTT/OPC-UA
Factory	Create new model/shadow types	ShadowManager.createShadow(); ModelManager.createModel()
Tier architecture	Organize layers (ingest, storage, model)	Explicit component grouping in TwinArch MTV/CTV
Shared Repository	Manage cross-module data exchange	DataManager façade; ContextBroker

Reusability is afforded by generic port-attachment and delegation styles, which allow modules to be reused or reconfigured across use cases and domains (e.g., using URDF-driven digital twins for heterogeneous robot arms (Audonnet et al., 2023)).

4. Computation, Synchronization, and Real-Time Operation

Temporal and behavioral aspects are managed through concurrent, event-driven, or GALS (Globally-Asynchronous Locally-Synchronous) semantics (Park et al., 2020). Here, modules within a clock-domain run synchronously; domains interact via FIFO buffers or message passing, supporting scalable, starvation-free scheduling and guaranteeing deterministic semantics within local micro-steps.

Surrogate modeling via neural networks (e.g., GRU sequence-to-sequence surrogates), allows for real-time or faster-than-real-time simulation, as the surrogate emulates physics-based component evolution and yields sub-second predictions for control and “what-if” analysis (Lim et al., 8 Jul 2025). Hybrid architectures combine physics-based solvers (e.g., SAM modules for thermal-fluid simulation), data-driven surrogates, and operator-augmentation via LLMs to enable predictive control, automated diagnostics, and natural-language operator guidance.

For multi-domain cyber-physical twins (e.g., smart grid cyberattack replication (Sen et al., 6 Dec 2024)), modular separation between power-grid simulation, network emulation, and process/SCADA is enforced via co-simulation drivers and standardized APIs, yielding scalability, reproducibility, and performance isolation.

5. Calibration, Adaptation, and Optimization

Calibration and adaptation of modular digital twins utilize either frequentist (e.g., mean squared error minimization) or Bayesian techniques, extended with mechanistic sensitivity analysis (e.g., Linear Noise Approximation) to quantify propagation of parameter errors from sub-models to overall system predictions (Cheng et al., 7 May 2024). Modular case-based reasoning (CBR) allows for retrieval, reuse, revision, and retention cycles for adaptation in manufacturing twins (Bolender et al., 2021). Multi-agent orchestration (JADE MAS) operationalizes Plan-Do-Check-Act (PDCA) cycles, balancing quality, cost, and runtime metrics via decentralized agent-based coordination for industrial adaptation and model selection/reconfiguration (Dittler et al., 2022).

Design of Experiments (DoE) policies leverage surrogate error-propagation derivatives to drive sample-efficient, interpretable sequential calibration—policy gradients steer experimental design toward maximally informative module configurations, optimizing global prediction accuracy.

6. Extension Mechanisms, Federation, and Scalability

Extension points, federation, and scalability are realized by:

• Ontology augmentation: Adding new ADEs or JSON-LD contexts for asset/property types.
• Federated asset delivery: Asset modules reference web locations or API endpoints, smoothly supporting multi-provider ecosystems across urban, industrial, or space systems (Tsampras et al., 15 Sep 2025, Wei et al., 4 Jun 2024, Gao et al., 13 Mar 2024).
• Horizontal scaling: Microservice architectures allocate each function (physical-layer emulation, path computation, analytics) as an independent service container, facilitating load-balanced deployments and integration with live hardware in satellite and SAGIN twins (Gao et al., 13 Mar 2024).
• Versioned traceability and model indexing: Systems like TwinArch or DTME stack (Somma et al., 10 Apr 2025, Wei et al., 4 Jun 2024) rely on Git repositories and graph stores (Neo4j/Hawk) for time-series querying, provenance capture, and scalable model management.

Decoupled scheduling and job coordination, e.g., contract-net MAS patterns, support runtime dynamic scaling and extension without central bottlenecks.

7. Evaluation and Applications Across Domains

Benchmarking modular digital twins follows domain-specific metrics: temperature RMSE, throughput, calibration accuracy, latency, and uncertainty estimation. For advanced nuclear SMR thermal-fluid twins, GRU surrogates achieve sub-second prediction with RMSE of 1.42 K for 16 temperature channels (Lim et al., 8 Jul 2025). In clinical DTs, ensemble fusion meta-models aggregate base-model predictions, validate with ROC AUC, F1, calibration, and decision curve metrics, all feeding back into model adaptation cycles (Nitschke et al., 2 May 2025).

Domain applications include (but are not limited to):

• Urban planning: federation of scene assets and context via NGSI-LD (Tsampras et al., 15 Sep 2025).
• Smart grids: offline attack simulation, rapid scenario replay, and cyber-physical security (Sen et al., 6 Dec 2024).
• Biomanufacturing: interpretable, multi-scale model integration and calibration (Cheng et al., 7 May 2024).
• Space systems: modular model-driven development across maturity levels—from design to federated AI-augmented twins (Wei et al., 4 Jun 2024).
• Patient care: evolving, explainable DTs leveraging ensemble learning and knowledge graphs for personalized assistance (Nitschke et al., 2 May 2025).
• Robotics: teleoperation via modular twin architectures, real-time synchronization, and controller/model plug-and-play (Audonnet et al., 2023).

A plausible implication is that modularity—supported by clearly scoped interfaces, traceable composition, and extensible ontologies—has become foundational for scaling digital twin adoption across sectors characterized by heterogeneity, continuous innovation, and demanding real-time performance requirements.