Apply now
Postgraduate DiplomaData, AI, Simulation & Analytics Applications open

Diploma in Uncertainty Quantification and Propagation

Online postgraduate diploma of 800 hours in uncertainty quantification and propagation: 8 modules covering uncertainty in engineering models, input characterisation, sampling-based propagation, spectral and non-intrusive methods, metamodels for UQ, sensitivity analysis, and calibration and inverse inference, with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
Enrol now View syllabus Ask an advisor
Virtual lab · try it before you enrol

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.

SIM · LIVE ●
Learning outcomes

What you will be able to do

01

Classify the sources of uncertainty in engineering models.

02

Characterise uncertain input variables using data and expert judgement.

03

Propagate uncertainties through sampling and spectral methods.

04

Build metamodels that reduce computational cost.

05

Calculate and interpret global sensitivity indices.

06

Calibrate parameters through Bayesian inference.

07

Express uncertainty results in line with the GUM and ASME V&V.

Profile

Who it's for

  • Uncertainty quantification engineers.
  • Simulation analysts who need to put confidence bounds on their results.
  • Structural reliability engineers.
  • Data scientists working in engineering.
  • Computational and analysis engineers.
  • Verification and validation specialists.
  • Engineering or science graduates aiming to enter the aerospace sector.
Standards & tools

What you'll work with

  • JCGM 100:2008 (GUM) (guide to the expression of uncertainty in measurement)
  • JCGM 101:2008 (GUM Supplement 1) (propagation of distributions using Monte Carlo)
  • ASME V&V 10-2019 (verification and validation in computational solid mechanics)
  • ASME V&V 20-2009 (verification and validation in computational fluid dynamics and heat transfer)
  • NASA-STD-7009A (standard for models and simulations)
  • ISO 5725-2:2019 (repeatability and reproducibility of measurement methods)
  • JCGM 200:2012 (VIM) (international vocabulary of metrology)
  • OpenTURNS
  • UQLab
  • Chaospy
  • Dakota
  • PyMC
Syllabus

8 modules

  1. 1.1Aleatory and epistemic uncertainty
  2. 1.2Sources in parameters, model and numerics
  3. 1.3Probabilistic and interval representation
  4. 1.4Dempster-Shafer evidence theory
  5. 1.5Probability boxes (p-boxes)
  6. 1.6Forward and inverse UQ problems
  7. 1.7Expression according to the GUM guide
  8. 1.8The ASME V&V 10 and V&V 20 framework
  9. 1.9Communicating uncertainty
  10. 1.10Workshop: uncertainty inventory of a thermal model
  1. 2.1Distribution fitting and goodness of fit
  2. 2.2Maximum likelihood estimation
  3. 2.3Expert judgement elicitation
  4. 2.4Correlations and copulas
  5. 2.5Random fields and the Karhunen-Loève expansion
  6. 2.6Distributions from scarce data
  7. 2.7The maximum entropy principle
  8. 2.8Dependent variables and the Rosenblatt transformation
  9. 2.9Documenting input assumptions
  10. 2.10Practical exercise: fitting copulas to materials data
  1. 3.1The Monte Carlo method and convergence
  2. 3.2Propagation according to GUM Supplement 1
  3. 3.3Latin hypercube sampling
  4. 3.4Sobol and Halton quasi-Monte Carlo sequences
  5. 3.5Multilevel Monte Carlo
  6. 3.6Importance sampling
  7. 3.7Estimating small failure probabilities
  8. 3.8Subset simulation for rare events
  9. 3.9Confidence intervals of estimators
  10. 3.10Lab: Monte Carlo propagation with OpenTURNS
  1. 4.1Generalised polynomial chaos
  2. 4.2Askey families and orthogonal bases
  3. 4.3Gaussian quadrature and Smolyak sparse grids
  4. 4.4Least-squares regression for PCE
  5. 4.5Sparse PCE with LARS
  6. 4.6Stochastic collocation at quadrature nodes
  7. 4.7Intrusive Galerkin methods
  8. 4.8The curse of dimensionality
  9. 4.9Post-processing of moments and densities
  10. 4.10Lab: polynomial chaos with UQLab or Chaospy
  1. 5.1Kriging and Gaussian processes
  2. 5.2Space-filling design of experiments
  3. 5.3Active learning and adaptive sampling
  4. 5.4Neural networks as surrogates
  5. 5.5POD reduced-order models
  6. 5.6Multi-fidelity and co-kriging
  7. 5.7Cross-validation of the metamodel
  8. 5.8The surrogate’s own uncertainty
  9. 5.9Selecting the right metamodel
  10. 5.10Practical exercise: kriging of a finite element model
  1. 6.1Local sensitivity and derivatives
  2. 6.2The Morris elementary effects method
  3. 6.3First-order and total Sobol indices
  4. 6.4Saltelli and Jansen estimators
  5. 6.5Indices from polynomial chaos
  6. 6.6Moment-based and density-based measures
  7. 6.7Sensitivity with dependent inputs
  8. 6.8Dimension reduction through sensitivity
  9. 6.9Interpretation for decision-making
  10. 6.10Case study: sensitivity of a corrosion model
  1. 7.1Bayesian parameter calibration
  2. 7.2The Kennedy-O’Hagan framework and model discrepancy
  3. 7.3Markov chain Monte Carlo
  4. 7.4Hamiltonian Monte Carlo and NUTS
  5. 7.5Approximate inference and ABC
  6. 7.6Updating with experimental data
  7. 7.7Prediction with posterior uncertainty
  8. 7.8FORM and SORM structural reliability
  9. 7.9Decisions under uncertainty
  10. 7.10Case study: Bayesian calibration of a fatigue model
  1. 8.1Selection of the model and quantities of interest
  2. 8.2Characterisation of uncertain inputs
  3. 8.3Building the metamodel
  4. 8.4Propagation and estimation of moments
  5. 8.5Global sensitivity analysis
  6. 8.6Calibration with available data
  7. 8.7Uncertainty report according to the GUM
  8. 8.8Conclusions for decision-making
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

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 it fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is OpenTURNS, UQLab, Chaospy, Dakota and PyMC, in free, academic or evaluation versions. Testing is studied on real procedures: uncertainty propagation in finite element models, Bayesian calibration with test data and sensitivity analysis of degradation models. Assessment is continuous by module, plus the final capstone project.

Standards covered: JCGM 100:2008 (GUM), JCGM 101:2008 (GUM Supplement 1), ASME V&V 10-2019, ASME V&V 20-2009, NASA-STD-7009A, ISO 5725-2:2019, JCGM 200:2012 (VIM).

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of JCGM 100:2008 (GUM), JCGM 101:2008 (GUM Supplement 1) and ASME V&V 10-2019 applied to real cases, not as a theoretical annex.
Industry software:OpenTURNS, UQLab, Chaospy, Dakota and PyMC, in free, academic or evaluation versions.
Final project:an uncertainty quantification project on a real model, from input characterisation and metamodelling to sensitivity analysis, calibration and a GUM-compliant uncertainty report, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Model and input characterisation
Select the model and quantities of interest and characterise the uncertain inputs.
Metamodel construction
Build and validate a metamodel that reduces the computational cost of the analysis.
Propagation and sensitivity
Propagate the uncertainties, estimate moments and run a global sensitivity analysis.
Calibration and reporting
Calibrate the model with available data and write the uncertainty report according to the GUM.
Final project
Present conclusions for decision-making together with the final report.
Career opportunities

Where this takes you

Uncertainty quantification engineerSimulation analystStructural reliability engineerEngineering data scientistComputational analysis engineerVerification and validation specialist
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • An 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.

Apply now Scholarships
FAQ

Frequently asked questions

The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with OpenTURNS, UQLab, Chaospy, Dakota 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), 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.

Keep exploring

Related programmes

See all in Data, AI, Simulation & Analytics
Postgraduate Diploma2,000 $

Diploma in OTA Security and Edge Version Management

Online postgraduate diploma of 800 hours in OTA security and version management at the edge. 8 modules covering edge device life cycle, root of trust and…

800 hours · 6–8 monthsOnlineData, AI, Simulation & Analytics
Postgraduate Diploma1,440 $

Diploma in Multimodal Pipelines and Synchronization

Online postgraduate diploma of 800 hours in multimodal pipelines and synchronisation: 8 modules covering multimodal data, time synchronisation with IEEE 1588 (PTP) and IEEE 802.1AS (gPTP),…

6–8 monthsOnlineData, AI, Simulation & Analytics
Postgraduate Diploma1,220 $

Diploma in V&V of Critical Models and Safety Cases

Online postgraduate diploma of 800 hours in V&V of critical models and safety cases: 8 modules covering verification and validation concepts, code and solution verification, validation…

6–8 monthsOnlineData, AI, Simulation & Analytics
Diploma in Uncertainty Quantification and PropagationEnrol
Related programmes

More in Data, AI, Simulation & Analytics

Diploma in Integration of Technical Assistants in OperationsView programme →Diploma in Classical and Deep RL for Dynamic SystemsView programme →Diploma in Discrete Simulation and Operational What-IfView programme →Diploma in Parallelization with CUDA/OpenMP and ProfilingView programme →Diploma in Micro/Macro-Sim of Networks and Traffic SignalsView programme →Diploma in MILP/CP Modeling and Heuristics for ProductionView programme →

All Data, AI, Simulation & Analytics programmes → · How to apply · Fees & financing · Scholarships

Scroll to Top
SEIUM – Institute of Advanced Engineering
Powered by  GDPR Cookie Compliance
Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.