and Validation Best Practices for Integrated Computational Materials
Due to the exponential increase in the availability of computing power over the last several decades, modeling and simulation have become an important part of virtually all of aspects of science, engineering, and manufacturing. For modeling and simulation to achieve the stated goal of Integrated Computational Materials Engineering (ICME) of delivering tools that aid the science and engineering decision-making process, thorough and unbiased assessments of the accuracy and credibility of model results are critical. This course gives an overview of the topics of verification and validation and uncertainty analysis, demonstrated by contextualized examples, with a focus on quantifying the confidence that can be attributed to results of computational materials science models. While these topics have been investigated for decades in the computational fluid dynamics and structural analysis communities, the ICME community is just starting to grapple with implementing these approaches, and will face some unique challenges due to the range of physical phenomena that are of interest to the materials science community.
Who Should Attend
Those who will benefit from this course include a broad cross section of ICME stakeholders, such as materials researchers, educators, design and manufacturing engineers, and program managers who seek to understand how to assess the accuracy of computational materials science and engineering simulations.
- Learn the concepts of uncertainty quantification for ICME model verification and validation (V&V)
- Understand the recommended process for assessing V&V needs
- Learn the commercial and open-source tools and resources available to aid with V&V implementation
- Master the concepts of state-of-the-art probabilistic and uncertainty quantification methods
- Understand what the results from a probabilistic/reliability analysis mean
- Verification and Validation Best Practices for Integrated Computational Materials