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About

  • 1. About
  • 2. Acknowledgments
  • 3. Copyright and License
  • 4. How To Cite
  • 5. Capabilities
  • 6. Release Notes
  • 7. Release Plans
  • 8. Glossary
  • 9. Abbreviations

User Manual

  • 1. Running Application
  • 2. Getting Started Tutorial
  • 3. User Interface
  • 4. Tools
  • 5. Examples
  • 6. Troubleshooting
  • 7. Requirements
  • 8. Bugs & Feature Requests
  • 9. Running in Your Browser
  • 10. Video Overview

Technical Manual

  • 1. Dakota Methods
  • 2. Methods in SimCenterUQ Engine
  • 3. Methods in UCSD UQ Engine

Developer Manual

  • 1. How to Build
  • 2. How to Extend
  • 3. Verification
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  • 1. Dakota Methods
  • 2. Methods in SimCenterUQ Engine
    • 2.1. Nataf transformation
    • 2.2. Global sensitivity analysis
      • Video Resources
      • Variance-based global sensitivity indices
      • Estimation of Sobol indices using Probabilistic model-based global sensitivity analysis (PM-GSA)
      • Dealing with high-dimensional responses with PCA-PSA
      • Aggregated sensitivity index
    • 2.3. Global surrogate modeling
      • Introduction to Gaussian process regression (Kriging)
      • Dealing with noisy measurements
      • Construction of the surrogate model
      • Adaptive Design of Experiments (DoE)
      • Verification of surrogate model
    • 2.4. Multi-fidelity Monte Carlo (MFMC)
      • Models with different infidelities
      • Pre-execution checklist for MFMC
      • Algorithm details
      • Speed-up
  • 3. Methods in UCSD UQ Engine
    • 3.1. Transitional Markov chain Monte Carlo
    • 3.2. Bayesian Inference of Hierarchical Models
      • Special Case: Normal Population Distribution
      • Sampling the Posterior Probability Distribution of the Parameters of the Hierarchical Model

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