Uncertainty quantification (UQ) is a scientific methodology used to determine and reduce uncertainties in both computational and real-world systems, enhancing the reliability of predictions and decision-making processes. It involves the integration of statistical, mathematical, and computational techniques to model and analyze the impact of input uncertainties on system outputs.

Uncertainty Quantification

Uncertainty quantification

Scientific methodology is a systematic approach to research that involves observation, hypothesis formulation, experimentation, and analysis to draw conclusions about the natural world. It is fundamental in ensuring that scientific inquiries are objective, reproducible, and verifiable, thereby advancing knowledge and understanding.

scientific methodology

reduce uncertainties

Computational Systems

real-world systems

reliability of predictions

Decision-making processes involve selecting the best course of action from multiple alternatives based on available information, preferences, and values. Effective decision-making requires balancing rational analysis with intuitive judgment to achieve desired outcomes and mitigate potential risks.

decision-making processes

Integration of statistical techniques

Mathematical Techniques

computational techniques

model and analyze

impact of input uncertainties

system outputs

Analyzing Simulation Outcomes

Importance of Validation in Simulation

Teaching Recommendation: Understanding Divergence in Predictions

Simulation Prediction Accuracy

Understanding Model Limitations

Risk Assessment in Simulation Projects

Selecting Input Variables in Simulation Experiments

Model Validation and Updating

Validation of Simulation Outcomes

Identifying Potential Risks

Identifying and Analyzing Potential Risks

Foundations of Risk Identification

Relevant Degrees

Your Lessons