Interactive Photonic Digital Twin

Updated 6 January 2026

Interactive Photonic Digital Twin is a cyber-physical framework that blends physics-based models and machine learning to simulate and optimize optical networks in real time.
It integrates live field measurements, dynamic parameter updates, and intent-based orchestration to enable autonomous lifecycle management and rapid failure diagnosis.
Demonstrated performance includes sub-1.5 s inference latency and GSNR error improvements, underscoring its scalability and operational robustness.

An interactive photonic digital twin (IPDT) is a cyber-physical framework that continuously couples a high-fidelity, software-based simulation and optimization environment ("the twin") with real-world optical communication infrastructures. By dynamically ingesting field measurements, updating physical and operational parameters, and providing real-time feedback to control and management planes, it enables autonomous lifecycle management, performance prediction, failure diagnosis, and closed-loop resource optimization for optical networks. Recent architectures integrate physics-informed modeling, data-driven machine learning, and intent-based orchestration for large-scale, multi-vendor photonic systems (Borraccini et al., 2022, Wang et al., 2020, Song et al., 28 Apr 2025).

1. System Architecture and Functional Components

An IPDT architecture consists of interlinked physical and digital domains with explicit feedback and orchestration loops (Borraccini et al., 2022, Wang et al., 2020, Song et al., 28 Apr 2025):

Physical Layer/Plant: White-box transponders (e.g., Cassini with CFP2-ACO/DCO), open reconfigurable optical add-drop multiplexers (ROADMs), programmable amplifiers (EDFAs), and multi-span fiber networks equipped with distributed sensors (OCM, OTDR) and environmental monitors.
Digital Twin Core:
- Physics-informed engine (PIE): Implements domain equations (GNLS, ASE noise, GSNR) using field-calibrated parameters.
- Data-driven engine (DDE): Employs deep neural networks (e.g., DeepONet, BiLSTM) for data-driven channel emulation, parameter identification, and real-time KPI prediction.
- Update manager (UM): Continuously synchronizes measured and predicted states, retrains models, adjusts parameters (e.g., Raman gain, frequency-dependent loss), and triggers recalculation on significant deviation.
- User interface & orchestration (UIO): REST/WebSocket APIs, dashboards, and an intent-based orchestration stack (ONOS/OONC) decouple user requests from detailed photonic configurations.
Control Plane Interfaces: SDN-based drivers (NETCONF/YANG, REST) for bidirectional communication, telemetry polling (typ. 1–15 s intervals), and device configuration.
Real-time databus: Message brokers (Kafka/MQTT), relational/time-series databases, and microservices coordinate bi-directional data and control flows.

A representative flow involves telemetry acquisition, parameter estimation, digital twin recalibration, real-time QoT computation, feedback to SDN controllers, and programmable network actuation, ensuring tight cyber-physical integration and operational agility (Song et al., 28 Apr 2025, Wang et al., 2020, Borraccini et al., 2022).

2. Mathematical and Algorithmic Foundations

IPDT platforms rigorously model both physical-layer propagation and control logic using a hybrid of physics-guided and data-driven methods.

Fiber and Amplifier Modeling:
- Power evolution for channel $n$ :
$\frac{\partial P_n(z)}{\partial z} + 2\alpha_n P_n(z) - r \sum_{m=1}^{N} \frac{g_R(f_m-f_n)}{A_\mathrm{eff}} P_n(z) P_m(z) = 0$

where $r$ is Raman scaling, $g_R$ is the Raman gain spectrum, $\alpha_n$ is span loss, and $A_\mathrm{eff}$ is effective fiber area (Song et al., 28 Apr 2025). - ASE noise per amplifier in bandwidth $B$ :

$P_{\mathrm{ASE}} = n_{sp} h \nu (G - 1) B$

where $n_{sp}$ is the spontaneous emission factor, $G$ is amplifier gain (Borraccini et al., 2022).
QoT Estimation:
- Generalized Signal-to-Noise Ratio (GSNR):
$\mathrm{GSNR} = \frac{P_{ch}}{\sigma^2_{\mathrm{ASE}} + \sigma^2_{\mathrm{NL}}}$

with $\sigma^2_{\mathrm{ASE}}$ from amplifier accumulation and $\sigma^2_{\mathrm{NL}}$ from nonlinearity (analytic GN model, $P_{ch}^3$ dependence) (Borraccini et al., 2022). - OSNR is similarly computed but excludes nonlinear impairments.
Deep Learning Models:
- Transmission simulation via BiLSTM/DeepONet: Mapping $x[n]$ (Tx signal) to $y[n]$ (Rx signal) with loss minimized by MSE (Wang et al., 2020, Song et al., 28 Apr 2025).
- Parameter identification and hybrid inference: Embedding ODE residuals in learning loss, alternating gradient steps on network parameters $\theta$ and physical parameters $\Lambda$ for joint physical consistency and data fit (Song et al., 28 Apr 2025).
- Decision making with Double DQN: State $s_t$ , action $a_t$ , value function $Q(s,a;\theta)$ , trained via deep RL for optimal hardware configuration (Wang et al., 2020).

3. Real-Time Operation, Dynamic Updating, and Closed Loops

IPDTs maintain synchrony with live networks through hierarchical, multi-timescale updating and feedback mechanisms (Borraccini et al., 2022, Song et al., 28 Apr 2025, Wang et al., 2020):

Calibration and Telemetry: Periodic polling from amplifiers, OCMs, environmental sensors; cognitive calibration via mismatches between simulated and measured spectra to regress fiber loss, connector loss, and gain drift.
Online Model Refinement: The update manager monitors prediction errors (e.g., $>0.5\,\mathrm{dB}$ margin), triggers parameter refinement cycles (micro-iterations of data and physics loss weighting), and convergence checking.
Intent-based Orchestration: North-bound requests (REST "intents") specify high-level connectivity/service goals, which trigger twin-assisted routing, modulation assignment (maximizing $M$ s.t. $\mathrm{GSNR} \geq \mathrm{GSNR}_\mathrm{req}(M)$ ), and provisioning commands to devices.
Failure Recovery and Event Handling: Automates lightpath recovery (e.g., fiber cut response in $<15$ s), with subcomponent OLCs adjusting EDFA/ROADM settings and PLASE/GNPy recalculating valid transmission configurations.
API and User Interactivity: Real-time dashboards, "what-if" simulators (e.g., live power margin evaluation for hypothetical configuration), actuator commands, and automated or operator-mediated fault management (Wang et al., 2020).

4. Provisioning, Fault Management, and Optimization Algorithms

Multiple algorithmic strategies enable provisioning, diagnosis, and resource optimization in IPDTs, leveraging the twin's real-time, multiscale modeling (Borraccini et al., 2022, Wang et al., 2020, Song et al., 28 Apr 2025):

Table: Selected Algorithms and Their Functions

Function	Algorithmic Approach	Key Input/Output
Lightpath computation	Per-path GSNR/max- $M$ scan	Path/ $\lambda$ /modulation tuple
Fault prediction	2-layer BiGRU + XGBoost	Temporal state $\to$ fault prob.
Hardware configuration	Double DQN (5-layer NN)	State vector $\to$ control cmd.
Transmission simulation	3-layer BiLSTM/DeepONet	Tx waveform $\to$ receive trace
Parameter ID/Updating	Alternating GD (physics+data)	$\max$ -likelihood fit, ODE loss

Provisioning proceeds via twin queries to calculate for each candidate route and wavelength the feasible modulation format, sorting by highest spectral efficiency and lowest distance, and allocating to satisfy bit-rate requests (Borraccini et al., 2022). Fault management fuses long-window sliding time-series via RNNs and rule-based classifiers (XGBoost), providing fast parameter forecasts and class probability outputs with scheduled retraining on detected drifts (Wang et al., 2020).

Optimization extends to adaptive EDFA gain control and spectrum allocation: deep RL agents optimize configuration actions to minimize spectrum use and latency, learning reward functions using real-time measurements and simulated environment rollouts (Wang et al., 2020). Physics-informed neural operators (PINNs, DeepONet) are embedded in the control loop, providing rapid QoT estimates with high consistency and sub-dB error margins (Song et al., 28 Apr 2025).

5. Experimental Validation and Performance Metrics

Field and simulation studies demonstrate the effectiveness of large-scale IPDT deployments (Borraccini et al., 2022, Song et al., 28 Apr 2025):

Throughput and Margins:
- For a 1000 km, 3-node triangle with 75 channels and hybrid DP-QPSK/16QAM, GNPy-predicted GSNRs closely track measured values with $0$–3.7 dB margin (shortest path), $0.0$–1.1 dB margin (long path).
Provisioning and Recovery Latency:
- End-to-end lightpath recovery, including path computation, amplifier control, and device configuration, achieved in $<12$ s (intent to restored traffic) (Borraccini et al., 2022).
Prediction Accuracy and Speed:
- Dynamic-updating DTs achieve maximum $1.4$ dB GSNR error improvement post-device replacement and up to $100\times$ speedup vs classical split-step Fourier (SSFM) solvers (1.1 s for DT inference vs 51 s SSFM for 120 km/2¹⁷ symbols) (Song et al., 28 Apr 2025).
- Generalization to unseen loading patterns with normalized RMSE~ $10^{-4}$ for large-scale topologies.
Operational Robustness:
- Zero-margin operation validated, conservative twin predictions ensure capacity is maintained without over-provisioning.
- Automated cognitive calibration supports live hardware replacement and brownfield operation, aligning twin parameters with real plant state (Song et al., 28 Apr 2025, Borraccini et al., 2022).
Scalability:
- End-to-end inference in 1.5 s for COST239 mesh (25 hops, 96 channels) using DeepONet hybrid approach (Song et al., 28 Apr 2025).

6. Real-World Interactivity, Implementation, and Lifecycle Integration

IPDT frameworks integrate continuous operation, real-time interactivity, and lifecycle management (Borraccini et al., 2022, Wang et al., 2020, Song et al., 28 Apr 2025):

Bidirectional mapping: All data are tagged by GPS-synchronized timestamps, guaranteeing coherent physical-digital alignment and enabling sliding-window data/model synchronization.
Software stack: Microservices include telemetry collectors (gRPC/REST), DT core (physics+ML), update managers, and web dashboards, interfaced via streaming protocols (Kafka/MQTT).
Deployment lifecycle:
- Greenfield: Twin initialized from manufactured parameters, refined via live data until errors $<0.2$ dB.
- Brownfield: Continuous polling with threshold-triggered updates; full versioning and rollback for safety.
- Retraining: Regular retraining (e.g., quarterly) with CI/CD pipelines and complete dataset augmentation.
User Interface & Orchestration: Operator GUIs and APIs provide dashboards for KPIs (per-span OSNR, GSNR, margin alarms), "what-if" sandboxes for configuration experiments, and maintenance modes for manual override.
Operational strategies: Conservative worst-case amplifier settings, full decoupling of control/data planes, and modular upgrades demonstrated to be compatible with metro and DWDM restoration SLAs.

7. Synthesis and Implications

IPDTs represent an advanced paradigm for autonomous, high-reliability optical network management, blending open-source physics-based emulation (GNPy), hybrid PINN/data-driven modeling (DeepONet, BiLSTM), and automated intent-driven orchestration (ONOS/OONC). Continuous closed-loop calibration and high-frequency telemetry empower rapid failure recovery, predictive maintenance, self-adaptive configuration, and network-wide optimization with field-proven accuracy and real-time performance (Borraccini et al., 2022, Wang et al., 2020, Song et al., 28 Apr 2025).

A plausible implication is that as hybrid data/physics approaches and scalable deep operator networks mature, IPDTs will become foundational for large-scale, low-margin, brownfield and greenfield photonic infrastructure, serving as persistent, actionable "sources of truth" for next-generation SDN-controlled optical systems.