34 citations · 71 across the 28 of their papers we have counts for
3 papers · 1 filter
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems
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 expe…
Statistics of extreme events in coarse-scale climate simulations via machine learning correction operators trained on nudged datasets
Alexis-Tzianni Charalampopoulos, Shixuan Zhang, Bryce Harrop +2
This work presents a systematic framework for improving the predictions of statistical quantities for turbulent systems, with a focus on correcting climate simulations obtained by…
Harnessing the instability mechanisms in airfoil flow for the data-driven forecasting of extreme events
Benedikt Barthel, Themistoklis Sapsis
This work addresses the data-driven forecasting of extreme events in the airfoil flow. These events may be seen as examples of the kind of unsteady and intermittent dynamics releva…