4 citations · 7 across the 2 of their papers we have counts for
2 papers
physics.comp-ph2023★ 3 cited
Turbulence Model Development based on a Novel Method Combining Gene Expression Programming with an Artificial Neural Network
Haochen Li, Fabian Waschkowski, Yaomin Zhao +1
Data-driven methods are widely used to develop physical models, but there still exist limitations that affect their performance, generalizability and robustness. By combining gene…
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…