The World's Forum for Aerospace Leadership

  • Donate
  • Press Room
  • Renew
  • View Cart
American Institute of Aeronautics and Astronautics

    • AIAA Governance
    • ARC
    • AIAA Foundation
    • Industry Guide
    Course Overview

    Verification and Validation in Scientific Computing


    The performance, reliability, and safety of engineering systems are becoming increasingly reliant on modeling and simulation. This course deals with techniques and practical procedures for assessing the credibility and accuracy of simulations in science and engineering. It presents modern terminology and effective procedures for verification of numerical simulations and validation of mathematical models that are described by partial differential equations. While the focus is on scientific computing, experimentalists will benefit from the discussion of techniques for designing and conducting validation experiments. A framework is provided for estimating various sources of errors and uncertainties identified both in simulations and in experiments, and then combining these in total prediction uncertainty. Application examples techniques and procedures are primarily taken from fluid dynamics, solid mechanics, and heat transfer. This short course follows closely the instructors’ new book Verification and Validation in Scientific Computing published by Cambridge University Press in 2010.

    Key Topics:

    • Learn terminology and basic principles in verification, validation and uncertainty estimation
    • Procedures for verification of codes
    • Procedures for verification of calculations
    • Design and execution of validation experiments
    • Quantitative assessment of model accuracy based on experimental measurements
    • Estimation of predictive uncertainty in simulations

    Who Should Attend:

    Computational analysts, code developers, experimentalists, and software quality engineers should attend the course. Also, managers directing such work and project engineers relying on computational simulation for risk-informed decision making should attend. An undergraduate degree in engineering or physics is recommended, and experience in computational simulation or experimental testing is helpful.

    Course Information:

    Type of Course: Instructor-Led Short Course
    Course Level: Intermediate

    Course scheduling available in the following formats:

    • Course at Conference
    • On-site Course
    • Stand-alone/Public Course

    Course Length: 2 days
    AIAA CEU's available: yes