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

Discovering and forecasting extreme events via active learning in neural operators

no code implementations • 5 Apr 2022 • Ethan Pickering, Stephen Guth, George Em Karniadakis, Themistoklis P. Sapsis

This model-agnostic framework pairs a BED scheme that actively selects data for quantifying extreme events with an ensemble of DNOs that approximate infinite-dimensional nonlinear operators.

Active Learning Experimental Design +1