This course is intended to help engineers with a basic knowledge of LS-DYNA and LS-OPT to become proficient in advanced optimization and probabilistic design methods. With this course we hope for you to become more productive at design and parameter identification of complex systems, such as multidisciplinary systems with competing objectives, advanced material testing and models, and systems with discontinuous responses. We will also provide insight into reliability and robustness to facilitate higher quality product design. Additionally, we will introduce classification-based adaptive sampling constraints as a tool for enhancing the efficiency.
In this course, we will discuss both the theoretical and practical aspects of design. We will cover advanced topics, such as multi-objective and collaborative optimization, digital image correlation, statistical classification, and probabilistic optimization. During workshop sessions, we will apply the discussed theoretical topics. We will use the LS-OPT graphical user interface to teach input preparation and post-processing. We will also emphasize interfacing with LS-DYNA.
- Required: Basic knowledge about direct and metamodel-based optimization and result analysis using LS-OPT.
- Strongly recommended: Introduction to LS-OPT class since it provides a foundation for some of the advanced topics.
- Recommended but not required: An introductory class in LS-DYNA for familiarity with a few keywords.
The teaching method is simple and practical to reduce the barrier to learn advanced topics. The class relies on simple class-room introduction of advanced topics in simple and incremental way to make it easier to understand all topics.