Course Overview
Simulation Verification, Validation & Uncertainty Quantification
Answer the most important simulation question: how much confidence should an engineer place in the result?
Build defensible simulation evidence through verification, validation, uncertainty quantification, sensitivity analysis, and model-credibility assessment.
Why This Course Matters
A model can be mathematically solved and still be a poor representation of reality. Engineering decisions require evidence that equations were solved correctly, the model represents the physical system adequately, and uncertainty has been understood rather than ignored.
Modern engineering teams increasingly need professionals who can connect theory with numerical modelling, simulation setup, verification, result interpretation, design analysis, and technical review. This course is designed to strengthen that capability with practical, engineering-focused learning.
What This Training Helps You Achieve
Build defensible simulation evidence through verification, validation, uncertainty quantification, sensitivity analysis, and model-credibility assessment. The training helps you apply the subject with stronger technical reasoning, clearer assumptions, and more confidence when supporting real engineering decisions.
Why Engineers Take This Course
Build stronger technical understanding
Strengthen your ability to work confidently with verification, validation, calibration, and prediction terminology, while understanding how the underlying assumptions affect practical engineering outcomes.
Apply the method to real engineering problems
Strengthen your ability to work confidently with input uncertainty and parameter variability, while understanding how the underlying assumptions affect practical engineering outcomes.
Make more defensible engineering decisions
Strengthen your ability to work confidently with building a simulation credibility argument and reporting confidence, while understanding how the underlying assumptions affect practical engineering outcomes.
What You’ll Explore
- Verification, validation, calibration, and prediction terminology
- Code verification and solution verification
- Discretisation, iterative, and numerical error
- Experimental validation and comparison metrics
- Input uncertainty and parameter variability
- Sensitivity analysis
- Uncertainty propagation and Monte Carlo concepts
- Model-form uncertainty and validation limitations
- Building a simulation credibility argument and reporting confidence
Learning Outcomes
By the end of this course, you will be able to:
- Explain and apply the core principles associated with verification, validation, calibration, and prediction terminology.
- Interpret engineering information related to code verification and solution verification.
- Evaluate practical considerations involving discretisation, iterative, and numerical error.
- Recognise key assumptions, limitations, and risks associated with input uncertainty and parameter variability.
- Use structured engineering judgement when working with model-form uncertainty and validation limitations.
- Connect analysis and technical evidence with building a simulation credibility argument and reporting confidence.
- Apply the principles and methods covered in this course with greater technical confidence, discipline, and credibility.
Who This Is For
- FEA, CFD, and multiphysics analysts
- Engineering simulation leads and technical authorities
- Mechanical, aerospace, energy, and structural engineers
- Engineers working in safety-critical or regulated sectors
- Technical reviewers who need stronger simulation-assurance frameworks
Why Build This Skill Now
Engineering teams are expected to make faster decisions while still demonstrating sound technical judgement, traceability, and awareness of uncertainty. Developing this capability provides a stronger basis for reviewing assumptions, challenging weak conclusions, and contributing more effectively when technical decisions matter.
If you want to strengthen your understanding of this subject, improve the quality of your engineering judgement, and build capability that can be applied across real projects, this course is a strong next step.
Related Topics
simulation verification, simulation validation, uncertainty quantification, V&V, model credibility, numerical verification, model validation, sensitivity analysis, uncertainty propagation, Monte Carlo simulation, model form uncertainty, simulation assurance, computational modelling, engineering simulation, validation metrics
