Gaussian Process (GP) models and deep learning (DL) each offer distinct advantages in machine learning (ML) for engineering simulations. GP models provide strong performance across small, medium, and large sample sizes, making them versatile in a wide range of scenarios. Deep learning, on the other hand, may outperform GP when handling highly complex response functions, such as those encountered in image-based tasks.
While traditional GP models struggle with large datasets, SmartUQ’s advanced GP models overcome these limitations, excelling in both training speed and accuracy. This makes them well-suited for large-scale problems that previously would have been out of reach for GP methods.
GP models are especially valuable for uncertainty quantification (UQ), as they inherently produce variance predictions that enable the generation of confidence intervals and support adaptive sampling techniques.
SmartUQ also provides comprehensive tools to compare the performance of GP and deep learning models on specific datasets, helping users choose the best method for their needs.
In this webinar, SmartUQ’s Principal Application Engineer, Gavin Jones, will delve into these tools, discussing the strengths, trade-offs, and practical applications of both GP and deep learning, with real-world examples using SmartUQ.
