60 citations · 67 across the 4 of their papers we have counts for
4 papers
A Surrogate-Augmented Symbolic CFD-Driven Training Framework for Accelerating Multi-objective Physical Model Development
Yuan Fang, Fabian Waschkowski, Maximilian Reissmann +3
Computational Fluid Dynamics (CFD)-driven training combines machine learning (ML) with CFD solvers to develop physically consistent closure models with improved predictive accuracy…
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…