training & development
Probabilistic and Uncertainty Quantification Methods for Model Verification & Validation

Probabilistic and Uncertainty Quantification Methods for Model Verification & Validation

Pricing and Availability

Pricing and dates are pending, please check back.



To learn more about the Verification and Validation Symposium, including venue information, please click HERE.  To download the course brochure, please click HERE.

This two-day course explains the concepts and effective procedures used for managing uncertainties in model predictions.  The focus is on methods, approaches, and strategies for quantifying uncertainties in model predictions.

Probabilistic and Uncertainty Quantification (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. 

Course attendees will be furnished with print copies of the presentation, as well as printed copies of the charts used during the course.  Attendees will also receive a 3-month license for the NESSUS® probabilistic analysis software.

You Will Learn To:
 - 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

Click HERE to review the course outline

Course Requirements
Attendees will need to bring a Windows or Apple based laptop computer to complete the course exercises. Download and installation instructions will be provided prior to the course or can be installed during the course if the attendee has administrator permissions.

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 Type: Masterclass
  • Order Number: MC146
  • Language: English
Final invoices will include applicable sales and use tax.


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. 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.

David Riha, is a Principal 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 for applied reliability problems, model verification and validation, and uncertainty quantification to 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 60 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.
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