FREE WEBINAR: Machine Learning for Narrowing the Simulation-Test Gap in Digital Twins

DATE: August 24, 2021
TIME: 03:00 PM EDT/ Noon PDT

Establishing how well a numerical simulation represents reality is critical for making simulation results more trustworthy for decision makers.

Sponsored by

 

Speaker

Gavin Jones
Principal Application Engineer
SmartUQ

 

Moderator

Moderator: Kenneth Wong
Senior Editor
Digital Engineering

This webinar will focus on statistical model calibration, a machine learning process used to quantify uncertainties (both parameter and model form) in simulations, a means to narrow the gap between simulation and physical test outcomes.

This process works not just for making simulations more accurate for traditional uses such as in the design phase, but also for making models more accurate when used as part of a digital twin workflow. Calibrated predictive models of a simulation can even themselves be used as digital twins, allowing for rapid predictions.

Using examples, this LIVE webinar with Q&A covers the underpinning ideas and benefits of statistical calibration such as:

• Unique ability to account for both parameter and model form uncertainties.
• Frequentist and Bayesian calibration options (available in SmartUQ)
• Applications of statistical calibration to digital twins.