GUI Desktop Manual

This manual provides a detailed breakdown of the functionality found inside the DigiQual Shiny application. For instructions on how to install or launch the app, see the Launch the App guide.

The DigiQual GUI is built with a modern “Fluent” design system and provides a visual alternative to the Python API. It is split into five functional tabs (alongside a Home dashboard) that map directly to the programmatic workflow.

1. Experimental Design

Purpose: Generate a statistically sound framework of sampling points for your physics solvers to evaluate.

Instead of writing scripts to generate arrays, you can visually define your parameters and automatically export a completed Latin Hypercube Sample (LHS).

  1. Add Variables: Click “Add Variable” for each continuous parameter in your simulation (e.g., Length, Angle).
  2. Define Bounds: Set the minimum and maximum physical limits for each parameter. The LHS algorithm will ensure these bounds are evenly covered.
  3. Specify Samples: Enter the total number of initial simulation runs you plan to execute (\(N\)).
  4. Generate Framework: The tool will instantly construct the multi-dimensional parameter space, attempting to maximize the minimum distance between points. Under the hood, the library automatically reorders coordinates using a greedy max-min distance algorithm. This ensures that any prefix of size \(k\) is as space-filling as possible, allowing you to stop the physical solver early or execute runs incrementally while maintaining optimal coverage of the parameter space.
  5. Download CSV: Save generated_sample.csv. You can now feed this CSV into your external solver tool (e.g., MATLAB, Abaqus, ANSYS) to calculate the actual signal responses.

2. Simulation Diagnostics

Purpose: Validate your dataset, detect input correlations, and identify regions where the physics solver failed to provide sufficient coverage or converged poorly.

  1. Upload Data: Upload the CSV containing your completed simulation results.
  2. Configure Columns: Select which variables are the inputs (e.g., Flaw Size, Angle) and which is the output (the Signal Response).
  3. Run Diagnostics: The engine evaluates the data against five rigorous statistical tests:
    • Gap Ratio: Checks for gaps in input space coverage.
    • R² Fit: Measures whether a basic surrogate model can capture a meaningful signal-to-noise relationship.
    • Average CV & Peak CV: Evaluates bootstrap prediction stability.
    • Collinearity Check: Calculates the Variance Inflation Factor (VIF) for each input variable to detect multicollinearity.
  4. Configure Thresholds: Adjust diagnostic tolerances (including a Max Allowed VIF setting, defaulting to 5.0) to define custom limits for passing.
  5. Collinearity Analysis: A dedicated card displays a correlation heatmap of your input variables, complete with VIF values and correlation coefficients, highlighting any strongly correlated variables that could destabilize model fitting.
  6. Remediation: If the data fails the coverage or collinearity checks, the app will offer an Active Learning “Remediation” tool or highlight issues. Enter the number of new samples you want, and it will generate a targeted CSV of new coordinates specifically designed to fill the detected gaps and stabilize the model.

3. Model Fit (Physics)

Purpose: Determine the mathematical structure of your parameters rapidly without heavy computation.

This tab represents Layer 1 and Layer 2 of the DigiQual caching architecture.

  1. Configure Parameters: Select up to 2 Parameters of Interest (to plot) and up to 2 Nuisance parameters (to integrate out). Unassigned parameters will automatically be held as constant slices.
  2. Nuisance Parameters Integration Profile: For each selected Nuisance variable, you can configure its statistical distribution: Uniform (defaulting to observed data limits), Normal (Gaussian), Lognormal, or Weibull distributions. The app estimates these parameters automatically from the uploaded data and displays a preview of the 95% bounds.
  3. Select Model: Choose “Auto (Best Fit)” to let Cross-Validation evaluate all Polynomials (Degrees 1-10) and Kriging, or force a specific model type.
  4. Fit Model: Click the fit button. The engine calculates the fundamental physics regression, optimizes the heteroscedastic variance (noise) profile, and saves the mathematical equations into the high-speed cache.
  5. Parameter Sensitivity (Sobol): Once fit, a Parameter Sensitivity (Total Effect) card appears. This displays the total-order Sobol indices (as percentages) for each parameter, ranking them by their direct impact on the signal response (including their multi-dimensional interactions).
  6. Diagnostics plots: The tab renders the best fitting model cross-validation score comparison, the signal fit surface, and a 4-panel residual validation diagnostic suite.

4. PoD Explorer (Reliability)

Purpose: Real-time reliability evaluation using pre-calculated Threshold Spectrums.

This tab relies entirely on Layer 3 and Layer 4 of the caching architecture. Because the heavy math was completed in Tab 3, exploring the reliability surface here is virtually instantaneous.

  1. Detection Threshold: Dragging this slider interpolates across a pre-calculated 100-threshold spectrum matrix, shifting the PoD S-Curve left and right instantly.
  2. Dynamic Slice Sliders: Any parameters you did not assign in Tab 3 will appear here as sliders. Moving a slider isolates a new 2D slice of your multidimensional space. The engine takes a vectorized “Fast Path” to evaluate the new physics and updates the PoD curve in milliseconds without needing to refit the core model.
  3. Side-by-Side View: Watch the Signal Response model (left) shift up and down alongside the PoD Curve (right) to intuitively understand the relationship between physics and reliability.

5. UQ Analysis (Confidence)

Purpose: Construct rigorous Probability of Detection bounds via parallelised Bootstrapping, and inspect threshold sensitivity and multi-confidence level reliability matrices.

Once you have used the PoD Explorer to find the exact threshold and parameter slice you care about, you move to this final tab to perform Uncertainty Quantification (UQ).

  1. Bootstrap Configuration: Define the number of resampling iterations (typically 1000). Ensure Parallel Compute is enabled to utilize all your CPU cores.
  2. Run UQ: The system safely bypasses the fast-caches. It locks the mathematical shape chosen in Tab 3 and runs thousands of Monte Carlo iterations to calculate the exact uncertainty bounds.
  3. Select Reliability Targets: Select a specific Target PoD (50%, 90%, 95%, 99%) and a Confidence Level (50%, 90%, 95%, 99%) to annotate and display the exact reliability sizing point (e.g., a90/95 or a90/90) on the curve.
  4. Reliability Index Matrix (aX/Y): When a single Parameter of Interest is analyzed, the app displays a complete matrix table showing the flaw sizing values required to reach standard combinations of Target Probability of Detection (rows) and Confidence Levels (columns).
  5. Threshold Sensitivity Plot: The app displays a curve plotting the Probability of Detection against the Detection Threshold for representative defect sizes, showing how changing the threshold affects reliability.
  6. Export Results: Download a comprehensive Excel workbook containing your configuration metrics, LaTeX equations, the raw numerical curve data, and the newly added Reliability Matrix spreadsheet tab.