MADISON, WI - June 11, 2018: SmartUQ received the award for Best Training Course at the Conference on Advancing Analysis & Simulation in Engineering (CAASE) hosted by the National Agency for Finite Element Methods and Standards (NAFEMS) and Digital Engineering Magazine.
CAASE is a three-day conference where developers and practitioners of CAE-related technologies share experiences and relevant trends and explore the future issues in the industry. SmartUQ presented three tutorials and four presentations at the event, sharing the latest in Uncertainty Quantification methods for the simulation industry.
The CAASE attendees were asked to vote for their favorite presentation and training course, and they selected SmartUQ “Uncertainty Quantification with Complex Data” as the best training course. This course discusses the challenges of complex data from such simulations and emerging technologies like Digital Thread – Digital Twin, the new UQ methods, and the results they yield.
SmartUQ presented two additional training courses: “Introduction to Probabilistic Analysis and Uncertainty Quantification” and “Catapulting Through Simulation Uncertainty with Model Calibration”.
Interested in learning about Uncertainty Quantification? SmartUQ in collaboration with NAFEMS Stochastic Working Group will be presenting “Introduction to Uncertainty Quantification and Industry Challenges” on July 24, 11:00 AM to 12:00 PM EDT. To register for the webinar, go to http://smartuq.com/resources/webinars/introduction-to-uncertainty-quantification-and-industry-challenges/.
For additional resources including white papers and on-demand webinars, check out SmartUQ’s website at http://smartuq.com/.
SmartUQ is a powerful engineering analytics software tool that incorporates real world variability and probabilistic behavior into the analysis of complex systems. The software utilizes breakthrough analytics techniques to rapidly quantify all forms of uncertainties. SmartUQ analytics tools accelerate design cycles by reducing design iterations, improving design robustness and maximizing insight of complex systems by quantifying uncertainties.