August 20–22, 2024

Online Course

Course Curriculum

The course will include six, half-day, virtual modules, with supporting materials. Registrants receive access to all course materials and access to the TMS science and technology accelerator study report, Accelerating the Broad Implementation of Verification & Validation in Computational Models of the Mechanics of Materials and Structures. Recordings and materials remain available to registrants online until October 31, 2024.

Course Schedule

The schedule is in Eastern Daylight Time (UTC-4:00). Use the Time Zone Converter to translate event times into your local time zone.

Course Modules

Module 1: How to Design and Implement Robust Verification and Validation Practices

Instructor: Michael Tonks

This module will introduce the TMS V&V accelerator study and report, summarize the basic concepts of V&V [report Section I], and go over the V&V value proposition [Section II]. It will instruct attendees on why V&V is critical for computational modeling of materials and structures and how to design and implement V&V practices. It will discuss the six process steps in any V&V approach, as outlined in the TMS V&V study report [Section III]. It will also work through the process of planning the six process steps for materials applications.

Learning Objectives

1. Understand why a robust V&V approach is essential for computational modeling of materials and structures
2. Learn the basics of a V&V approach, including the six V&V process steps and how they interact
3. Gain practical experience in planning the six V&V process steps

Format: Live instruction. Breakout sessions for a hands-on planning of the six process steps. Survey.

Module 2: Code and Solution Verification

Instructors: Michael Tonks

This module will cover both code verification and solution verification elements considered in the TMS V&V accelerator study report. This module will instruct attendees on how to carry out both code and solution verification. It will begin with how to carry out code verification by comparing to analytical and manufactured solutions. Students will then learn how to carry out solution verification to estimate the discretization error in a simulation that will be validated.

Learning Objectives

1. Learn the theory behind code verification, including the method of manufactured solutions
2. Gain practical experience carrying out code verification
3. Learn the theory behind solution verification
4. Gain practical experience carrying out solution verification

Format: Live Instruction and hands-on activities.

Module 3: Uncertainty Quantification: Computational Models

Instructor: Aaron Tallman

This module will cover a high-level overview of computational uncertainty quantification (UQ), what types of questions/problems it can address, the typical categories of methods used, and the practical and theoretical considerations most relevant to orienting ones work within the field..

Learning Objectives

1. Learn to track information (and uncertainty) through models both forwards and backwards (inverse)
2. Apply examples of forward UQ and inverse UQ to simple interactive examples
3. Discuss some commonly encountered issues in computational UQ
4. Explore advanced applications of computational UQ in materials modeling

Format: Live instruction

Module 4: Uncertainty Qualification: Experimental Data

Instructor: Brandon M. Wilson

This module will cover the quantification of uncertainty in experimental data. Examples will be provided on how to quantify the contributions of different factors toward the overall uncertainty in experimental data. These may include sources of experimental uncertainty related to non-repeatability of measurements, sensor imperfections resulting in noisy sensor outputs, error and uncertainty in machine outputs, errors arising from experimental data analysis and/or manipulation, etc.

Learning Objectives

1. Learn about sources of uncertainty in experimental data
2. Learn about uncertainty quantification methods for different uncertainty sources and measures of system behavior
3. Learn about aggregating the uncertainty resulting from multiple sources in experiments

Format: Live instruction

Module 5: Designing Validation Experiments: Combining Modeler and Experimentalist Perspectives

Instructors: Somnath Ghosh, Jacob Hochhalter, and Zachary Harris

This will cover process steps 2 and 6 in the TMS V&V accelerator study report. More specifically, this module will present a discussion of the preliminary calculations and design of validation experiments aspects of the verification TMS V&V accelerator study. The experiment-design stage comes early in the VV&UQ process and requires a close collaboration between modelers and experimentalists. The primary objective in this stage is to design an experiment wherein the quantities of interest (QoIs) that serve as a validation basis are defined. The discussion of the main concepts will be complemented with a few examples from several current in-practice cases to provide context. The module will conclude with a presentation of transitioning from validation to materials qualification.

Learning Objectives

1. Understand the difference between verification, calibration and validation and their importance and place in the overall VV&UQ process
2. Understand how to scope the objectives of a validation task to address the desired application
3. Practice hands-on experiment design using open-source computational tools

Format: Live instruction and hands-on activity.

Module 6: Regulatory Agency Perspectives: Examples and Lessons Learned

Instructors: David Moorcroft, Kenneth Aycock, and Joshua Kaizer

This module will give the perspective of the importance and utility of VVUQ and provide examples in certain commercial sectors from the point of view of three different regulatory agencies: the Federal Aviation Administration, the U.S. Nuclear Regulatory Commission, and the US Food and Drug Administration. The instructors will discuss their experiences reviewing modeling and simulation, regulations regarding modeling and simulation, and approaches commonly used to assess the credibility of simulations.

Learning Objectives

1. Learn the importance of credible simulation data for use in regulatory decision making
2. Learn how VVUQ supports regulatory decision making when submissions include computational models
3. Participate in an open discussion on credibility, VVUQ, and regulatory perspectives

Format: Live instruction

For More Information

For more information about this course, please contact: