IPPD841 - Verification and Validation in Scientific Computing has been added to your cart.

Verification and Validation in Scientific Computing

Learn techniques and methods for verification of numerical simulations, validation of mathematical models, and quantify uncertainty in simulations.

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

{{ onlyLocationDate }}
This product is offered through an ASME partner.
Please complete your transaction through their site
{{ errorMessage }}

Final invoices will include applicable sales and use tax.

Print or Share

Course Options

  • Location and Date
    Seats Left
    List Price
    Member Price
  • 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.


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

Scientific computing plays an increasingly important role in determining the expected behavior in engineered systems and manufacturing processes. However, there is a frequent disconnect between simulations and what happens in reality. Without this awareness and understanding, decision makers could unknowingly put their customers, the public, the environment, or the system itself at risk. This course presents modern terminology, techniques, and effective methods for verification of numerical simulations, validation of mathematical models, and uncertainty quantification of nondeterministic simulations.

Computational analysts, designers, and project managers who rely on simulation for decision making are shown practical techniques and methods for assessing simulation accuracy, credibility, and total uncertainty of the simulation. The techniques presented in this course are applicable to a wide range of engineering and science applications, including fluid dynamics, heat transfer, solid mechanics, and structural dynamics. The mathematical models considered are given in terms of partial differential or integral equations, formulated as initial and boundary value problems. The computer codes that implement the mathematical models can use any type of numerical method (e.g., finite volume, finite element) and can be developed by commercial, corporate, government, or research organizations. 

While the focus of the course is on modeling and simulation, experimentalists will benefit from a detailed discussion of techniques for designing and conducting high quality validation experiments. The course will also discuss the responsibilities of organizations and individuals serving in various positions where scientific computing software, mathematical models, and simulation results are produced. 

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

  • Implement procedures for code verification and software engineering.
  • Implement procedures for solution (calculation) verification.
  • Plan and design model validation experiments.
  • Comprehend the concepts and procedures for nondeterministic (stochastic) simulation.
  • Identify sources of uncertainty in simulation, including both aleatory and epistemic uncertainties. 

Who Should Attend
This course benefits model developers, computational analysts, code developers, researchers, software engineers from physical and chemical sciences, and all regulatory bodies. Additionally, managers directing this work and project engineers relying on computational simulations for decision-making will also find this course to be beneficial. An undergraduate or advanced degree in engineering or the physical sciences is highly recommended. Training and experience in computational simulation of physical systems is also recommended.

Course Materials (included in purchase of course)

  • Verification and Validation in Scientific Computing, Cambridge University Press, 2010, written by Dr. William Oberkampf and Dr. Christopher Roy
  • Digital course notes via ASME’s Learning Platform

Course Materials (not included in purchase of course)

  • You must bring a laptop to class.

Topics covered in this course include:

  • Introduction to verification and validation
  • Introduction to uncertainty quantification
  • Code verification
  • Solution verification
  • Model validation experiments
  • Model accuracy assessment
  • Predictive capability of scientific computing
  • Simulation-informed decision-making

A certificate of completion will be issued to registrants who successfully attend and complete the course.


Day One

Introduction to Verification and Validation

  • Terminology and fundamental concepts
  • Credibility in scientific simulation

Introduction to Uncertainty Quantification

  • Concept of nondeterministic simulation
  • Example of nondeterministic simulation
  • Decision making under uncertainty

Code Verification

  • Software engineering
  • Order of accuracy of numerical solutions
  • Use of traditional and manufactured solutions

Solution Verification

  • Iterative convergence and error estimation
  • Discretization error estimation
  • Reliability of discretization error estimators

Day Two

Model Validation Experiments

  • Validation fundamentals
  • Validation experiment hierarchy
  • Validation experiments vs. traditional experiments
  • Six characteristics of model validation experiments

Model Accuracy Assessment

  • What are model validation metrics?
  • Recommended characteristics for validation metrics
  • Model validation and decision making

Predictive Capability of Computational Simulation

  • Identify all sources of uncertainty
  • Characterize each source of uncertainty
  • Estimate solution error in system responses of interest
  • Estimate total uncertainty in system responses of interest
  • Procedures for updating model parameters
  • Types of sensitivity analysis

Simulation-Informed Decision Making

  • Planning and prioritization in modeling and simulation
  • Maturity assessment of modeling and simulation
  • Business aspects of implementing simulation in decision-making

Christopher Roy, Ph.D.

Professor, Virginia Tech

Christopher Roy, Ph.D., 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.

William Oberkampf, Ph.D.

W. L. Oberkampf Consulting

William Oberkampf, Ph.D. is an engineering consultant with 50 years of experience in research and development in fluid dynamics, heat transfer, flight dynamics, and solid mechanics.

More Information



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
Buying for your team?

Set up a customized session of this course for your workforce.

You are now leaving ASME.org