Courses Category: Aerospace R&D

Sensitivity Analysis, Uncertainty Propagation, and Validation for Computational Models

Synopsis:

Computational modeling is becoming more prevalent in the engineering analysis and design process. This increased reliance means that one must understand the accuracy of the computational models. A first step in understanding the accuracy is to identify the model input parameters for which the computational model is most sensitive. The course will specifically focus on the following techniques for determining sensitivity information: differentiation of analytical models, finite difference of computational models, complex step method, software differentiation, sensitivity equation methods, adjoint methods, and sampling methods (Monte Carlo and Latin Hypercube). Techniques for propagating uncertainty in model inputs through computational models will also be presented. Uncertainty propagation techniques that use the sensitivity information (first-order techniques) and more general techniques based on sampling are covered in the course. The final topic covered is validation of computational models. Validation is a process to assess the accuracy of computational models by comparing to experimental data.

Key Topics:

  • Insights and application of sensitivity analysis
  • Approaches for computing sensitivity coefficients
  • Uncertainty propagation through computational models
  • Sampling-based methods for propagating uncertainty and performing sensitivity analysis
  • Methodology for validation of computational models

Who Should Attend:

This course is intended for engineering analysts that are faced with determining the sensitivity of computational models to parameters in their models. The minimum background is a BS in engineering (or related field). Managers directing the activities of staff responsible for sensitivity analysis would also benefit from this course.

Systems Thinking for Modern Aerospace Complexity

This comprehensive 2-day course covers systems thinking for addressing complexity in the development of modern aerospace systems. Applying a systems approach provides insight into the unexpected ways a system will behave due to complexity. Learning how to deal with scale, interdependencies and interconnectedness in large systems uncovers leverage points for managing complexity. The course covers complexity management from a number of perspectives, including organizational learning, quantitative metrics and system visualization techniques.

Verification and Validation Best Practices for Integrated Computational Materials Engineering

Synopsis

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.

Why Attend

  • 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 Engineering

Financial and Business Acumen for Navigating the Aerospace Industry

  • This new, practical course will develop students’ “business mindset” so they can make sound business/financial decisions for their organizations, and successfully navigate the various funding avenues available to them, while also setting the foundation for leadership positions within their organizations.
  • The course consists of lectures and in-class exercises using the public financial statements of several firms within the aerospace, defense, and space industries.
  • All students will receive an AIAA Certificate of Completion at the end of the course.

Foundations of CFD with OpenFOAM®

Instructed by Mr. Matej Forman, OpenFOAM® training team leader, Keysight Technologies

  • Learn OpenFOAM® CFD Fundamentals for Aerospace with a combination of lectures and associated hands-on lab exercises
  • All course notes and exercises will be available for download. No sessions will be recorded.
  • All students will receive an AIAA Certificate of Completion at the end of the course

Fundamentals of Python for Engineering Programming and Machine Learning (Starts 27 October 2026)

Instructed by Dr. Peng Ho, Boeing Designated Expert on engineering software development

  • From 27 October–19 November 2026 and 3 December 2026. No class on 3 November. (8 Classes, 32 Total Hours)
  • Tuesdays and Thursdays from 12–4 p.m. Eastern Time (all sessions will be recorded and available for replay; course material will be available for download)
  • The final eighth session on Tuesday, 3 December is for reviews, questions and interactive code development (following an intermission for material/code reflection).
  • All students will receive an AIAA Certificate of Completion at the end of the course