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L3 · Twin Modeling & AI

GNNs, PINNs, anomaly ensembles, MPC, and the KRONOS-CTRL digital twin with its predictive shadow.

L3 · Twin Modeling & AI — its place in the stack (left, click any layer) and its internal components (right). Telemetry rises; control descends.

What this layer does

GNNs, PINNs, anomaly ensembles, MPC, and the KRONOS-CTRL digital twin with its predictive shadow. Every page in this section is part of the same control stack — click any card to go deeper, or return to the Master Blueprint to see how it connects.

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Active Learning and Continual Retraining Actuator Allocation Across Coupled Systems Anomaly-Detection Ensembles Autoencoder Reconstruction Residuals Benchmarking PINNs Against Finite-Element Solvers Calibrating the Twin to Its Machine Collocation Sampling and Adaptive Refinement Confidence Scoring Across the Twin Coupling the Four Twin Modules Cross-Machine and Cross-Device Transfer Learning Detecting Shadow-to-Plant Divergence Detecting Sub-Threshold Magnet-Quench Precursors Disruption Precursors in the Breeder Free-Boundary Equilibrium and Coil Currents Fusing the Diagnostics Constellation GNN Imputation of Dropped and Degraded Signals Graph Neural Networks for Dynamic Sensor Topology Handoff Between Anomaly Detection and MPC Hard and Soft Constraints in MPC Hard-Constraint PINNs for Boundary Conditions Hybrid GNN-PINN Models KRONOS-CTRL: MHD Stability Module KRONOS-CTRL: Neutronics Module KRONOS-CTRL: Power Systems Module KRONOS-CTRL: The Digital Twin Stack KRONOS-CTRL: Thermomechanics Module L3 Contrasted: Breeder vs Burner L3 Twin Modeling and AI: Architecture Overview L3 for the Breeder: Disruption Avoidance L3 for the Breeder: Equilibrium Control L3 for the Breeder: The ELM-Free Negative-Triangularity Shape L3 for the Burner: Direct Energy Conversion L3 for the Burner: End-Plug Density L3 for the Burner: The Ambipolar Potential MPC for Breeder Plasma-Shape Control MPC for Burner End-Plug Density MPC for DEC Voltage and Grid Synchronization Message Passing Across the Diagnostic Constellation Mirnov Coils and Flux Loops as a Coupled Graph Model-Predictive Control: The Optimization Monitoring and Correcting Twin Drift Neural Operators for Field Surrogates PINNs Solving the Grad-Shafranov Equilibrium PINNs for MHD Stability Analysis PINNs for the Burner Ambipolar Potential Quantifying Precursor Lead Time REBCO Strain-Gauge Anomaly Detection Real-Time Equilibrium Reconstruction with PINNs Receding Horizon and Real-Time Feasibility Reduced-Order Models for the Twin State Estimation for the Twin Sub-Threshold Alarming and False-Alarm Control Surrogate Acceleration of the Twin Surrogate Models Inside the MPC Loop Synchronizing the Shadow to the Real Plant Terminal Sets and Recursive Feasibility The 50-100 ms Predictive Shadow The Certified Safe Operating Envelope The PINN Loss: Residual, Boundary, and Data Terms The Shadow Latency Budget The Twin State Vector and Interfaces Twin Fidelity Metrics Twin-Governed Fuel Cycle and Isotope Balance Uncertainty Quantification for Surrogates Verification and Validation of the Twin

L3 · Twin Modeling & AI

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