22 citations · 41 across the 6 of their papers we have counts for
6 papers
A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics
Benedikt Barthel Sorensen, Alexis Charalampopoulos, Shixuan Zhang +3
Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully-resolved climate simulations remain com…
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…
Hybrid quadrature moment method for accurate and stable representation of non-Gaussian processes and their dynamics
Alexis-Tzianni Charalampopoulos, Spencer H. Bryngelson, Tim Colonius +1
Solving the population balance equation (PBE) for the dynamics of a dispersed phase coupled to a continuous fluid is expensive. Still, one can reduce the cost by representing the e…
Machine-learning energy-preserving nonlocal closures for turbulent fluid flows and inertial tracers
Alexis-Tzianni G. Charalampopoulos, Themistoklis P. Sapsis
We formulate a data-driven, physics-constrained closure method for coarse-scale numerical simulations of turbulent fluid flows. Our approach involves a closure scheme that is non-l…
A Gaussian moment method and its augmentation via LSTM recurrent neural networks for the statistics of cavitating bubble populations
Spencer H. Bryngelson, Alexis Charalampopoulos, Themistoklis P. Sapsis +1
Phase-averaged dilute bubbly flow models require high-order statistical moments of the bubble population. The method of classes, which directly evolve bins of bubbles in the probab…
Implementation of a fully nonlinear Hamiltonian Coupled-Mode Theory, and application to solitary wave problems over bathymetry
Ch. E. Papoutsellis, A. G. Charalampopoulos, G. A. Athanassoulis
This paper deals with the implementation of a new, efficient, non-perturbative, Hamiltonian coupled-mode theory (HCMT) for the fully nonlinear, potential flow (NLPF) model of water…