26 citations · 75 across the 11 of their papers we have counts for
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physics.comp-ph2022★ 4 cited
Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models
Fabian Waschkowski, Haochen Li, Abhishek Deshmukh +5
Learning accurate numerical constants when developing algebraic models is a known challenge for evolutionary algorithms, such as Gene Expression Programming (GEP). This paper intro…
physics.flu-dyn2022★ 15 cited
Predictive data-driven model based on generative adversarial network for premixed turbulence-combustion regimes
Temistocle Grenga, Ludovico Nista, Christoph Schumann +4
Premixed flames exhibit different asymptotic regimes of interaction between heat release and turbulence depending on their respective length scales. At high Karlovitz number, the d…