Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems

no code implementations • 27 Jun 2023 • Stephen Guth, Alireza Mojahed, Themistoklis P. Sapsis

Machine learning methods for the construction of data-driven reduced order model models are used in an increasing variety of engineering domains, especially as a supplement to expensive computational fluid dynamics for design problems.

Gaussian Processes Uncertainty Quantification