IPPD842 - Probabilistic and Uncertainty Quantification Methods for Model Verification & Validation has been added to your cart.

Probabilistic and Uncertainty Quantification Methods for Model Verification & Validation

ASME's in-person course covers articulating precise approximation & assumption statements, quantifying the total uncertainty, & making risk-informed decisions.

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  • College Station, TX May 13-14th, 2024



Welcome Back!

The ability to interact with ASME instructors who bring real world experience, examples, and best practices to life in our learning experiences is a major reason learners choose face to face training. Networking with peers is also a valuable part of the time spent together during a course. We are excited to start offering these important courses again in person.

Schedule: ​This course commences at 8:30 AM and ends at 5 PM local time, each day, with breaks scheduled throughout. 

Venue: This course will be held at the Texas A&M Hotel and Conference Center in College Station, TX, in conjunction with the Verification, Validation, and Uncertainty Quantification Symposium. Please follow this link for hotel reservations and to learn more about the Symposium.


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Uncertainty quantification (UQ) methods are essential for designers, engineers, and scientists to make precise statements, as well as quantify numerically, the degree of confidence they have in their simulation-based decisions.
Quantifying the total uncertainty in a simulation will ensure decision-makers can measure the credibility of a prediction and proactively work to save time, allocate resources, and reduce the risk of inadequate safety, reliability, or performance of the system.  Everyone has a model but it is challenging to communicate the approximations, assumptions, and uncertainties that exist in any model prediction.  Utilizing a UQ framework will provide you with a consistent and proven way of using the uncertainty in model predictions to make risk informed decisions. 
This course explains the concepts and effective procedures used not only to predict uncertainties in a model, but to also mature your model and build trust in your organization by being able to communicate and document your findings. This systematic framework focuses on methods, approaches, and strategies for quantifying uncertainties in model predictions.
APPLY what you learn! Probabilistic and UQ methods are presented in-depth followed by exercises to reinforce the material. Attendees learn how to use the NESSUS probabilistic analysis software and apply it throughout the course to gain experience in problem formulation and results interpretation and communication. 

By participating in this course, you will learn how to successfully:

  • Identify potential uncertainties in models and data.
  • Represent uncertainties in models and inputs.
  • Explain how uncertainties impact model predictions.
  • Select methods to efficiently propagate uncertainties in the models.
  • Identify options to reduce uncertainties in the model predictions.

Who should attend?
This course is essential for engineers, scientists, and technical managers concerned with managing uncertainties in model predictions used to make decisions in the engineering design and evaluation process.

Course Materials (included in purchase of course)

  • Digital course notes via ASME’s Learning Platform
  • Access to NESSUS software for 90 days. Download and software installation instructions will be provided prior to the course and can be installed prior or during the course.

Required Course Materials (not included with course, purchase separately)

  • Attendees will need a Windows based laptop computer to complete the course exercises.
  • Attendees must have administrator permissions in order to install the software.

Topics covered in this course include:

  • Modeling uncertain variables
  • Propagating uncertainties
  • Formulating Uncertainty Qualification (UQ) problems
  • Sensitivity analysis
  • UQ for numerical models
  • Response surface models for efficient uncertainty propagation
  • Bayesian statistics for uncertainty quantification
  • Model Parameter Calibration
  • UQ solution strategy examples and case studies

Topics Covered


  • Verification and validation
  • Validation metrics
  • Validation requirements
  • Predictions
  • Decisions

Modeling uncertain variables

  • Mathematical models for uncertainty (PDF/CDF)
  • Data fitting

Propagating uncertainties

  • Sampling methods 
  • Analytical methods 

Formulating UQ problems

  • Solution objectives 
  • Defining the model 
  • Modeling random variables 
  • Evaluating results 

Sensitivity analysis

  • Deterministic 
  • Probabilistic 
  • Global 

UQ for numerical models​

  • Uncertain variables related to finite element 
  • Modeling spatial and temporal variables 
  • Solution approaches 

Response surface models for efficient uncertainty propagation 

  • Basic principles 
  • Training data bounds/# points in practice 
  • Polynomial model fitting 
  • Gaussian process concepts 
  • Model assessment 

Bayesian statistics for uncertainty quantification and calibration

  • Identification and categorization of different types of uncertainty
  • Modeling/quantification of uncertainty
  • Bayesian analysis
  • Bayesian analysis for model calibration
  • Evaluation of calibration assumptions

UQ solution strategy examples 

  • Turbine blade model overview 
  • Model and data uncertainties 
  • Solution strategies 
  • Results interpretation 

V&V case study

  • V&V approach and plan
  • Model documentation, phenomena identification, and hierarchies
  • V&V and UQ assessments
  • Uncertainty reduction
  • Model validation and assessment

David Riha

Principal Engineer, Southwest Research Institute

David Riha, is a Principal Engineer at Southwest Research Institute. His technical expertise is in the area of predicting probabilistic response and reliability of engineered systems using advanced probabilistic and uncertainty methodologies.

Erin C. DeCarlo, Ph.D.

Research Engineer, Southwest Research Institute

At Southwest Research Institute, Dr. DeCarlo leads and provides her expertise on projects that involve probabilistic analysis, uncertainty quantification and reduction, and validation of both statistical and physics-based models.

Ben H. Thacker, Ph.D., P.E.

Executive Director, Southwest Research Institute

Ben H. Thacker, Ph.D., P.E., Director, Materials Engineering Department, Southwest Research Institute, brings over 25 years of expertise in computational mechanics, structural reliability, and computer methods development.

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Conducted in a physical classroom or lab with an instructor and peers.  

Note: ASME in-person activities will follow the state and local laws, regulations and guidelines regarding COVID-19 applicable to the location of the event.  Learn more here
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