60 citations · 105 across the 4 of their papers we have counts for
4 papers
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…
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…
Multi-Objective CFD-Driven Development of Coupled Turbulence Closure Models
Fabian Waschkowski, Yaomin Zhao, Richard Sandberg +1
This paper introduces two novel concepts in data-driven turbulence modeling that enable the simultaneous development of multiple closure models and the training towards multiple ob…
Evolution of material surfaces in the temporal transition in channel flow
Yaomin Zhao, Yue Yang, Shiyi Chen
We report a Lagrangian study on the evolution of material surfaces in the K-type temporal transitional channel flow. Based on the Eulerian velocity field from the DNS, a backward-p…