Advancements in simulation software, high-performance computing, and data sensors have led to great possibilities, but the massive amounts of data collected from these technologies are complex, high-dimensional, and difficult to validate with basic analytic tools. SmartUQ’s latest release includes tools that can handle even larger and more complex data sets than ever before. This webinar will walk through the latest features and how they can reduce the computational burden of performing advanced analytics on large data sets. Additionally, using a simulation based engineering design problem as a case study, we will cover the basics of design of experiments and sampling, surrogate model/emulator generation, and advanced analytics techniques like uncertainty propagation and sensitivity analysis.
Attendees for this webinar would be engineers, data scientists, and program managers involved in or overseeing simulation design and analyses and experimental analyses. Webinar attendees should gain an understanding of how to reduce computational burden of large data sets with intelligent data sampling tools, to quantify both epistemic and aleatory uncertainties in their simulation and test data, and to perform advanced analytics on complex, high-dimensional data sets.
