VCPD842 - Probabilistic and Uncertainty Quantification Methods for Model Verification & Validation (Virtual Classroom) has been added to your cart.

Probabilistic and Uncertainty Quantification Methods for Model Verification & Validation (Virtual Classroom)

Articulate precise approximation & assumption statements, quantify the total uncertainty, and make risk-informed decisions with any model. 

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Probabilistic and Uncertainty Quantification Methods for Model Verification & Validation (Virtual Classroom)
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Enroll now to save 62%

$1,830 $695

Enroll in the Verification & Validation of Models and Simulations Combo Course  to save $495. Learn more.

Schedule: This course runs from 9:30 AM and 1:30 PM and 2 PM – 6 PM Eastern each day, with breaks scheduled throughout.

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 will learn how to use the NESSUS probabilistic analysis software and will 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
  • Software access to NESSUS for 90 days Attendees will need a Windows computer to complete the course exercises.  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

This ASME Virtual Classroom course is held live with an instructor on our online learning platform.
Certificates of completion will be issued to registrants who successfully attend and complete the course.

Course Outline

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

Erin C. DeCarlo, Ph.D. is a Research Engineer in the Mechanical Engineering Division at Southwest Research Institute. Her research focuses on understanding the impact of uncertainty on design performance and making uncertainty-informed decisions using advanced probabilistic methods. Her work in response surface modeling and Bayesian statistics addresses the fundamental challenges of the computational expense of complex, multidisciplinary simulations, and uncertainty due to limited data in order to guide activities to target and reduce uncertainties.

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. She received her Ph.D. in Civil Engineering from Vanderbilt University in 2017 and recently co-led SwRI’s webinar series on “Probabilistic Analysis and Uncertainty Quantification”.
David Riha, is a Staff Engineer in the Mechanical Engineering Division at Southwest Research Institute.  His technical expertise and interests are concentrated in the area of predicting the probabilistic response and reliability of engineered systems using advanced probabilistic and uncertainty methodologies.  Since 1991, he has developed probabilistic methods and software tools including the NESSUS® probabilistic analysis software. 
Mr. Riha provides consulting and leads programs for applied reliability problems, model verification and validation, and uncertainty quantification for various industry and government agencies in areas such as aerospace, automotive, biomechanics, geomechanics, and weapon systems.  He also develops and presents training in the area of probabilistic analysis and design methods, uncertainty quantification, and approaches for model verification and validation. He has taught over 75 courses since 1991.  He has a B.S in aerospace engineering from the University of Texas at Austin and M.S. in mechanical engineering from the University of Texas at San Antonio.
Ben H. Thacker, Ph.D., P.E., Executive Director, Materials Engineering Department, Southwest Research Institute, brings over 30 years of expertise in computational mechanics, structural reliability, and computer methods development.  He has been heavily involved in the development and application of probabilistic methods and has applied probabilistic methods to geo-mechanics, biomechanics, and other transient non-linear problems. 
Dr. Thacker is an active member of the AIAA Non-Deterministic Technical Committee and the ASME Standards Committee on Verification and Validation.  He has instructed at the “Probabilistic Analysis and Design: Computational Methods and Applications” annual short course at the Southwest Research Institute since its inception.  He received his Ph.D. in Civil Engineering from University of Texas at Austin.


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