Enhancing Decision Making in MDO through Interactive Visual Analytics on the Web

In the context of Multidisciplinary Design Optimization (MDO), the use of data visualization and data analytics is critical for the understanding of complex interactions between variables and multi-objective functions in high-dimensional (>3) spaces. Current Visual Analytics (VA) techniques provide powerful interactive tools to analyze general-purpose data in many scientific and business contexts. However, the application of these methods in MDO contexts is less explored. Direct application of existing methods can easily overwhelm the user, mainly due to a) incorrect preprocessing of raw data, b) use of incorrect tools, or c) a combination of the aforementioned factors. To overcome these challenges, this manuscript aims to explore the application of some relevant 2D and 3D visualization techniques in the context of MDO. To achieve this goal, this manuscript presents some of the best state-of-the-art tools and discusses best practices for data processing. In addition, the tools presented are implemented in a client-server web environment where the heavy work (data preprocessing) is carried out by a Python-based server while the visualization tasks are left to the client. Ongoing work includes the integration and deployment of the presented methods in an interactive visualization framework for the analysis of MDO results.