Master’s in Model Verification, Validation and UQ Engineering
Online master's programme of 1,900 hours in model verification, validation and uncertainty quantification (UQ) engineering. 12 modules covering model credibility, code and solution verification, uncertainty propagation, sensitivity analysis, surrogate models, calibration and V&V of machine learning models under ASME V&V 40-2018, ASME V&V 10-2019 and NASA-STD-7009B, with an assigned technical tutor.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Plan model credibility according to the context of use.
Verify codes and numerical solutions with standard methods.
Characterise aleatory and epistemic uncertainties.
Propagate uncertainties using sampling and polynomial chaos methods.
Carry out global sensitivity analysis.
Calibrate and validate models against experimental data.
Apply V&V techniques to machine learning models.
Who it's for
- Simulation engineers who need to demonstrate the credibility of their models.
- Uncertainty quantification specialists looking for a standards-based framework.
- V&V engineers working in regulated industries.
- Data scientists in modelling who want rigorous validation methods.
- Certification engineers who rely on evidence from simulation.
- Technical risk analysts dealing with uncertain models.
- Engineering or science graduates looking to enter the aerospace sector.
What you'll work with
- ASME V&V 40-2018 (credibility of computational models for medical devices)
- ASME V&V 10-2019 (V&V in computational solid mechanics)
- NASA-STD-7009B (standard for models and simulations)
- ASME V&V 20-2009 (V&V in computational fluid dynamics and heat transfer)
- JCGM 100:2008 (GUM, guide to the expression of uncertainty in measurement)
- JCGM 101:2008 (propagation of distributions using a Monte Carlo method)
- ISO/IEC 25010 (software product quality model)
- ISO 2394 (general principles on reliability for structures)
- UQLab
- SALib
- Dakota
- OpenTURNS
- PyMC
12 modules · 1900 h
1.1Why and when to trust a model1.2Verification, validation and uncertainty quantification1.3Context of use and model risk1.4ASME V&V 40 credibility frameworks1.5NASA-STD-7009 standard for models and simulations1.6Validation hierarchy1.7Simulation-based decisions1.8Credibility documentation1.9Famous failures caused by unvalidated models1.10Case study: credibility of an implant model
2.1Unit testing of scientific software2.2Method of manufactured solutions2.3Observed order of accuracy2.4Comparison with analytical solutions2.5Regression testing2.6Code configuration management2.7Software quality according to ISO/IEC 250102.8Simulation code review2.9Continuous integration for simulation2.10Lab: verification with manufactured solutions
3.1Discretisation errors3.2Mesh convergence studies3.3Richardson extrapolation3.4Grid convergence index (GCI)3.5Iterative errors and stopping criteria3.6Round-off errors3.7A posteriori error estimation3.8Error-driven adaptive meshing3.9Numerical verification report3.10Practical: GCI study on a CFD case
4.1Aleatory and epistemic uncertainty4.2Input distributions and elicitation4.3Bayesian inference of uncertain parameters4.4Maximum likelihood methods4.5Intervals and p-boxes4.6Dempster-Shafer theory4.7Measurement uncertainty according to the GUM4.8Propagation by the Monte Carlo method4.9Dependencies and copulas4.10Lab: measurement uncertainty with the GUM and Monte Carlo
5.1Monte Carlo and stratified sampling5.2Latin hypercube and quasi-Monte Carlo5.3Polynomial chaos expansion5.4Stochastic collocation methods5.5Perturbation methods5.6Multilevel Monte Carlo5.7Rare events and importance sampling5.8FORM and SORM reliability methods5.9Choosing the method according to cost5.10Workshop: polynomial chaos propagation in a thermal model
6.1Local sensitivity and derivatives6.2Morris method6.3Sobol indices6.4FAST spectral sensitivity method6.5Moment-based sensitivity6.6Selecting influential parameters6.7Sensitivity with surrogate models6.8Interpreting results6.9Use in model simplification6.10Practical: Sobol indices with SALib
7.1Polynomial regression and response surfaces7.2Gaussian processes and kriging7.3Neural networks as surrogates7.4POD reduced-order models7.5Design of computer experiments7.6Cross-validation of surrogates7.7Multi-fidelity surrogate models7.8Active learning to enrich the surrogate7.9Surrogate uncertainty7.10Lab: Gaussian process for an expensive model
8.1Parameter calibration8.2Kennedy-O'Hagan Bayesian calibration8.3Model discrepancy8.4Sampling with MCMC methods8.5Validation metrics8.6Area metric and comparison of distributions8.7Design of validation experiments8.8Uncertainty of experimental data8.9Extrapolation beyond the validated domain8.10Workshop: Bayesian calibration with MCMC
9.1Differences from physics-based models9.2Out-of-distribution validation9.3Probability calibration9.4Conformal prediction with coverage guarantees9.5Bayesian networks and ensembles9.6Dropout as a Bayesian approximation9.7Robustness and adversarial testing9.8Formal verification of networks9.9Documenting the context of use9.10Practical: conformal prediction on a regression model
10.1Medical devices and in silico simulation10.2FDA guidance on computational modelling10.3Aerospace and certification by analysis10.4Nuclear and code safety10.5Automotive and virtual validation10.6Defence and model accreditation10.7Civil engineering and the Eurocodes10.8In silico models in pharmacometrics10.9Cross-sector lessons10.10Case study: credibility evidence for an authority
11.1Data assimilation11.2Ensemble Kalman filter11.3Online Bayesian updating11.4Continuous validation of the twin11.5Detecting loss of validity11.6UQ for maintenance and operation11.7Communicating uncertainty11.8Dashboards with confidence bands11.9Decisions with uncertain models11.10Lab: data assimilation in a thermal twin
12.1Selecting the model and context of use12.2Credibility plan12.3Code and solution verification12.4Characterising uncertainties12.5Propagation and sensitivity12.6Calibration and validation12.7Credibility assessment12.8Conclusions on credibility12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so you can combine the programme with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is UQLab, SALib, Dakota, OpenTURNS and PyMC, in free, academic or evaluation versions. Real procedures are studied: mesh convergence studies with GCI, Bayesian calibration of a simulation model, and global sensitivity analysis with Sobol indices. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ASME V&V 40-2018, ASME V&V 10-2019, NASA-STD-7009B, ASME V&V 20-2009, JCGM 100:2008 (GUM), JCGM 101:2008, ISO/IEC 25010 and ISO 2394.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with UQLab, SALib, Dakota, OpenTURNS and PyMC, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme software, and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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