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.
  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 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 will evaluate the data against four rigorous statistical tests (Gap Ratio, R² Fit, Average CV, Peak CV).
  4. Remediation: If the data fails the coverage checks, the app will offer an Active Learning “Remediation” tool. 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. Select Model: Choose “Auto (Best Fit)” to let Cross-Validation evaluate all Polynomials (Degrees 1-10) and Kriging, or force a specific model type.
  3. Fit Model: The engine calculates the fundamental physics regression, optimizes the heteroscedastic variance (noise) profile, and saves the mathematical equations into the high-speed cache.

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.

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 wrap it in 95% Confidence Intervals.

  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 integrations to calculate the exact uncertainty bounds.
  3. Export Results: Download a comprehensive Excel workbook containing your configuration metrics, LaTeX equations, and the raw numerical curve data across separate spreadsheet tabs.