How to Validate Machine Learning for Tokamak Plasma Disruption
Overview of Technical Issues:
The prediction model has an insufficient validation function for converting plasma sensor measurements into disruption forecasts, meaning its reliability and accuracy for safety-critical tokamak operation cannot be confirmed; the goal is to establish robust validation methods that verify the model can reliably predict disruptions before deployment in real-time plasma control systems where false negatives risk equipment damage and false positives cause unnecessary operational interruptions.
Solution directions generated for this problem
Problem Direction 1 :
ImproveValidation metric measurement precision
VSConstraintValidation cycle duration
Inspiration 1 : Cross-domain reference
Application Principle: #26 Copying
Cross-domain applicability
Contextual auto-completion for assistant systems
Innovative Solution Refine solution
Physics-informed synthetic disruption library for accelerated validation precision
Generate synthetic disruption library using validated physics codes
How to solve :
- Deploy NIMROD or M3D-C1 magnetohydrodynamic codes to generate 5,000+ synthetic plasma trajectories spanning operational space (β=0.5-4.0, q95=2.5-6.0, density 0.2-1.2 Greenwald limit)
- each trajectory labeled with disruption timing ±50ms precision
- Apply prediction model to synthetic library, compute ROC curves, precision-recall metrics, and timing error distributions with statistical confidence intervals (n>1000 per regime)
- establish baseline performance map across parameter space within 2-3 weeks computational time
- Validate synthetic-derived metrics against 200-300 experimental shots from historical databases (DIII-D/JET archives)
- confirm correlation coefficient ≥0.85 between synthetic and experimental performance
- use experimental data only to calibrate precision thresholds, not exhaustive testing
Expected Effect : Precision detection of 5-10% performance differences achieved in 3-4 weeks vs 3-6 months; validation cycle compressed 75%
Risk Control :
- synthetic-experimental correlation below 0.85 threshold
- physics code fidelity gaps in edge scenarios
- computational resource availability constraints
Problem Direction 2 :
ImproveTest scenario coverage breadth
VSConstraintValidation resource requirements
Inspiration 1 : Cross-domain reference
Application Principle: #1 Segmentation
Cross-domain applicability
Test scenario library construction methods, devices, electronic equipment and media
Innovative Solution Refine solution
Risk-stratified cluster sampling validation framework for disruption prediction models
Cluster plasma scenarios by disruption physics and risk level
How to solve :
- Segment plasma operational space into physics-based clusters (high-beta disruptions, density-limit events, locked modes, vertical displacement events) using dimensionality reduction on diagnostic parameter space (βN, q95, ne/nGW, frad)
- select representative samples from each cluster using Latin hypercube sampling with 8-12 shots per cluster instead of exhaustive coverage, reducing experimental shot requirements by 65%
- prioritize high-consequence clusters (VDEs, thermal quench >2MJ/m²) for deeper validation (15-20 shots) while accepting coarser sampling (5-8 shots) for low-risk routine disruptions
- validate model performance metrics (TPR ≥92%, FPR ≤8%, warning time ≥30ms) within each cluster independently using bootstrapped confidence intervals (95% CI)
- quality control: cluster separation verified by Silhouette coefficient ≥0.6, sample representativeness confirmed by Kolmogorov-Smirnov test (p>0.05) comparing sample vs full cluster distributions
- implementation steps: (1) extract 15-year historical shot database from DIII-D/JET (>50,000 discharges), (2) apply k-means clustering (k=6-8) on normalized diagnostic trajectories 100ms pre-disruption, (3) compute cluster risk scores from equipment damage records, (4) allocate 80-120 total validation shots proportional to risk×uncertainty, (5) execute stratified sampling and compute per-cluster performance with statistical significance testing
- compared to uniform random sampling requiring 300-400 shots for equivalent confidence, this approach achieves comprehensive coverage with 80-120 shots (70% reduction) while maintaining statistical power for safety-critical regimes
Expected Effect : Shot requirements -65%, coverage maintained across all disruption physics regimes, validation cost reduced 3.2×
Risk Control :
- cluster boundary ambiguity in hybrid disruption modes
- insufficient historical data for rare edge-case clusters
- sampling bias if cluster definitions drift with operational regime evolution
Problem Direction 3 :
ImproveModel prediction reliability quantification
VSConstraintValidation cycle duration
Inspiration 1 : Cross-domain reference
Application Principle: #11 Beforehand cushioning
Cross-domain applicability
Drive controller for motor of compressor and method of operating same
Innovative Solution Refine solution
Bayesian sequential reliability certification with adaptive stopping criteria
Establish conservative reliability bounds before validation starts
How to solve :
- Construct Bayesian priors for false negative/positive rates using historical tokamak disruption databases (DIII-D, JET archives spanning 10,000+ shots) and physics-based disruption models (NIMROD, M3D-C1 simulations)
- set initial conservative bounds at 99% confidence intervals as provisional safety thresholds
- Implement sequential probability ratio testing where each new experimental shot updates posterior distributions in real-time
- deploy model to real-time control when posterior false negative rate crosses below equipment damage threshold (≤0.5%) and false positive rate below operational interruption threshold (≤5%) with 95% credible intervals
- Apply adaptive stopping rules that terminate validation when posterior uncertainty reduction per additional shot falls below 2% relative improvement, avoiding unnecessary testing once reliability quantification achieves certification criteria — typically 40-60% fewer shots than fixed-sample frequentist approaches
Expected Effect : Validation time reduced from 12-16 weeks to 5-7 weeks; experimental shot requirements decreased 50-70%; reliability quantification achieves 95% confidence with conservative safety margins
Risk Control :
- Prior distribution misspecification risk
- posterior convergence to incorrect bounds
- computational overhead in real-time Bayesian updates
Problem Direction 4 :
ImproveModel prediction reliability quantification
VSConstraintValidation resource requirements
Inspiration 1 : Cross-domain reference
Application Principle: #24 Intermediary
Cross-domain applicability
Diagnostic applications using nucleic acid fragments
Innovative Solution Refine solution
Bayesian proxy metric validation for disruption prediction reliability
Use calibrated confidence as reliability proxy
How to solve :
- Validate model's internal confidence calibration (predicted probability vs actual disruption rate) on 50-100 experimental shots across key plasma regimes
- establish calibration curve with ≤5% binning error as proxy for false negative/positive rates
- Construct Bayesian priors from historical tokamak databases (DIII-D/JET archives: 10,000+ shots) encoding physics-based disruption likelihoods
- update priors with limited new validation shots (n=50-100) to quantify tail probability confidence intervals
- Deploy stratified sampling: allocate 70% of validation shots to high-consequence scenarios (vertical displacement events, thermal quench), 30% to routine disruptions
- accept ±10% error bounds for low-risk events, ±2% for equipment-damaging events
Expected Effect : Resource reduction 60-70%; reliability quantified to 95% confidence; false negative rate <1% for critical events
Risk Control :
- calibration curve overfitting to limited shots
- prior distribution misspecification from historical data
- stratification misses emerging disruption modes
Problem Direction 5 :
ImproveTest scenario coverage breadth
VSConstraintMust not deteriorate
Inspiration 1 : Cross-domain reference
Application Principle: #10 Preliminary action
Cross-domain applicability
Dynamically re-configurable in-field self-test capability for automotive systems
Innovative Solution Refine solution
Phased validation protocol with pre-deployment scenario library construction
Pre-deployment scenario library construction
How to solve :
- Phase 0 (Pre-deployment): Construct comprehensive scenario library from historical DIII-D/JET/EAST databases, categorizing 2000+ shots into 15-20 physics-based clusters (high-beta disruptions, density-limit events, locked modes, vertical displacement events) with automated feature extraction
- establish baseline performance distributions and identify coverage gaps before formal validation begins
- Phase 1 (Breadth screening, weeks 1-3): Execute single-shot sampling across all 15-20 clusters using pre-prepared test configurations, achieving 100% regime coverage with ≤50 experimental shots
- automated metrics compute prediction accuracy, timing error, and false alarm rate per cluster with acceptance threshold ±8% from baseline
- flag underperforming clusters for Phase 2
- Phase 2 (Depth validation, weeks 4-8): Concentrate remaining 150-200 shots on flagged clusters, performing 10-15 repeated tests per critical regime to achieve 95% confidence intervals on false negative rate <2% and false positive rate <5%
- Bayesian updating refines reliability bounds using Phase 0 priors, reducing required shots by 60% versus frequentist approaches
Expected Effect : Coverage: 100% regime breadth in 3 weeks, depth validation in 8 weeks total; resource efficiency: 200 shots vs 600 in exhaustive testing; reliability quantification: 95% confidence with pre-loaded priors
Risk Control :
- Phase 0 library completeness insufficient for rare disruption types
- cluster boundary definition subjectivity affecting Phase 1 screening accuracy
- Bayesian prior miscalibration biasing Phase 2 confidence intervals
