59 citations · 271 across the 20 of their papers we have counts for
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Constructing Neural Network-Based Models for Simulating Dynamical Systems
Christian Møldrup Legaard, Thomas Schranz, Gerald Schweiger +6
Dynamical systems see widespread use in natural sciences like physics, biology, chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics…
Model-form uncertainty quantification in RANS simulations of wakes and power losses in wind farms
Ali Eidi, Reza Ghiassi, Xiang Yang +1
Reynolds-averaged Navier-Stokes (RANS) is one of the most cost-efficient approaches to simulate wind-farm-atmosphere interactions. However, the applicability of RANS-based methods…
Determining a priori a RANS model's applicable range via global epistemic uncertainty quantification
Xinyi Huang, Naman Jain, Mahdi Abkar +2
Calibrating a Reynolds-averaged Navier-Stokes (RANS) model against data leads to an improvement. Determining {\it a priori} if such an improvement generalizes to flows outside the…