Simulation plays an indispensable role in engineering activities to achieve objectives such as rapid prototyping, optimization of designs and processes, and the analysis of complex systems without the need for costly physical testing. However, the effective use of simulation still presents several challenges including:
Surrogate modeling is an approach which can help address all the above. With surrogate modeling, first a machine learning (ML) model is trained to predict the simulation’s results. The ML model is then used in place of the simulation to run the desired analyses. The ML model’s rapid prediction of simulation results allows more analyses to be performed and in less time.
The success of surrogate modeling requires fast training speed and high prediction accuracy. Without speed, training a model can become infeasible as the scale and complexity of the problem increases. Without high accuracy a ML model’s predictions will have too much uncertainty to be usable. Worse, poor predictive accuracy can lead to decisions being made based on inaccurate information. This can result in catastrophic consequences. SmartUQ addresses the need for speed and accuracy with its best in class ML models. Further, SmartUQ’s accuracy and speed advantages are augmented by a flexible approach featuring many unique ML models, specifically designed to handle cases common to engineering simulation.
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will provide an introduction to SmartUQ’s fast, accurate, and flexible surrogate models.
