VCPD843 - Verification & Validation of Models and Simulations Combo Course (Virtual Classroom) has been added to your cart.
Courses

Verification & Validation of Models and Simulations Combo Course (Virtual Classroom)

Verify, validate, and quantify uncertainty, assess credibility, and make risk-informed decisions for models and simulations. 

This Standard was last reviewed and reaffirmed in {{activeProduct.ReaffirmationYear}}. Therefore this version remains in effect.

Verification & Validation of Models and Simulations Combo Course (Virtual Classroom)
{{ onlyLocationDate }}
{{ errorMessage }}

Final invoices will include applicable sales and use tax.

Print or Share
Description

Package Items
Quantity Item
{{ package.Quantity }} {{ package.Title }}

Enroll now to save 63%

$3,520 $1,295

The modeling process itself introduces participants to uncertainties due to a variety of factors and approximations made during modeling simulations. The techniques in this series of courses will give the learner the techniques and procedures for verification of numerical simulations, validation of mathematical models, and uncertainty quantification for assessing the credibility and total uncertainty of the predicted performance, reliability, and safety of engineering systems. Combined participants will make more effective, risk-informed decisions for models and simulations. 

 
This official ASME learning path consists of two courses:
 
Course 1: Verification and Validation in Scientific Computing 
In ASME’s Verification and Validation in Scientific Computing course, learn modern terminology, practical techniques, and procedures for verification of numerical simulations, validation of mathematical models, and uncertainty quantification for assessing the credibility of the predicted performance, reliability, and safety of engineering systems.

Course 2:  Probabilistic and Uncertainty Quantification Methods for Model Verification & Validation 
Learn effective procedures and the systematic way to predict uncertainties in models in ASME’s Uncertainty Quantification Methods for Model Verification & Validation course.

Who should attend?
This course benefits model developers, computational analysts, code developers, experimentalists, and software engineers. Managers directing this work and project engineers relying on computational simulations for decision-making will also find this course to be beneficial. The course will discuss the responsibilities of organizations and individuals serving in various positions where computational simulation software, mathematical models, and simulation results are produced. An undergraduate or advanced degree in engineering or the physical sciences is recommended. Training and experience in computational simulation or experimental testing is also helpful.
 
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.

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

Buying for a team? Get ASME Corporate Training.

Set up a customized session of this course for your workforce. Contact learningsolutions@asme.org to learn more about group rates.

Expand
Instructors

William Oberkampf, Ph.D. is an engineering consultant with 43 years of experience in research and development in fluid dynamics, heat transfer, flight dynamics, and solid mechanics.  He spent his entire career in both computational and experimental areas.  During the last 20 years, Dr. Oberkampf emphasized research and development in methodologies and procedures for verification, validation, and uncertainty quantification in computational simulations.  He has written over 177 journal articles, book chapters, conference papers, and technical reports. He has taught 44 short courses in the field of verification, validation, and uncertainty quantification.
 
Dr. Oberkampf received his B.S. in Aerospace Engineering in 1966 from the University of Notre Dame, his M.S. in Mechanical Engineering from the University of Texas at Austin in 1968, and his Ph.D. in 1970 in Aerospace Engineering from the University of Notre Dame.  Dr. Oberkampf served on the faculty of the Mechanical Engineering Department at the University of Texas at Austin for nine years.  After 29 years of service in both staff member and management positions at Sandia National Laboratories, he retired as a Distinguished Member of the Technical Staff.  Since this time, he has been a consultant to the National Aeronautics and Space Administration, the U.S. Air Force, various Department of Energy laboratories, and corporations in the U.S. and Europe. He is a fellow of the American Institute of Aeronautics and Astronautics
 
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.
 
Professor Christopher Roy, Virginia Tech, holds a B.S. in Mechanical Engineering from Duke University, an M.S. in Aerospace Engineering from Texas A&M University, and a Ph.D. in Aerospace Engineering from North Carolina State University.  From 1998 to 2003, he worked as a senior member of the technical staff in the Engineering Sciences Center at Sandia National Laboratories in Albuquerque, New Mexico.  From 2003 to 2007, he was an Assistant Professor in the Aerospace Engineering Department at Auburn University.
 
In 2007, Dr. Roy joined the Aerospace and Ocean Engineering Department at Virginia Tech and currently holds the rank of full professor.  He has written over 120 journal articles, books, book chapters, conference papers, and technical reports in the areas of verification, validation, and uncertainty quantification.  He has taught 30 short courses in the field of verification, validation, and uncertainty quantification.
 
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. 
 

Expand

Get papers online in the ASME Digital Collection

Find eBooks, journals, and proceedings papers
Subscribe to RSS feeds
Sign up for content alerts
View calls for papers

You are now leaving ASME.org