Engineers involved in product development, manufacturing, and maintenance know how challenging it is to use and interpret data to capture the real-world behavior of a system. Often the primary motivation for using such data is to generate a predictive capability that accurately mimics reality. Having an accurate predictive model enables the performance of advanced analytics including design space exploration, uncertainty analysis, trade studies, and predictive maintenance. These predictive capabilities can significantly reduce product development, warranty, and sustainment costs and have a tremendous impact on product reliability and durability.
Advancements in and the rapid proliferation of modeling and simulation, physical testing instrumentation, and digital measuring devices have led to new technologies such as the Internet of Things (IoT) or Digital Twins and have given engineers a “data rich” environment for conducting predictive analytics. Industries such as aerospace, automotive, heavy equipment, and medical devices are all seeing rapid growth in the size, dimensionality, and complexity of their data sets. Moreover, data from larger, more complex problems can include combinations of spatial, transient, or temporal responses. This webinar introduces the topic of predictive analytics and discusses the industry challenges and benefits that come from using these methods for engineering systems.
Using use cases and SmartUQ software for illustrative purposes, this webinar will discuss:
The webinar will conclude with a Q&A session.
