## Abstract

In this work recent advancements are presented in utilising deterministic symbolic regression to infer algebraic models for turbulent stress-strain relation with sparsity-promoting regression techniques. The goal is to build a functional expression from a set of candidate functions in order to represent the target data most accurately. Targets are the coefficients of a polynomial tensor basis, which are identified from high-fidelity data using regularised least-square regression. The method successfully identified a correction term for the benchmark test case of flow over periodic hills in 2D at Re_{h} = 10595.

Original language | English |
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Title of host publication | Proceedings of the 6th European Conference on Computational Mechanics |

Subtitle of host publication | Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018 |

Editors | Roger Owen, Rene de Borst, Jason Reese, Chris Pearce |

Publisher | International Centre for Numerical Methods in Engineering, CIMNE |

Pages | 1789-1795 |

Number of pages | 7 |

ISBN (Electronic) | 9788494731167 |

Publication status | Published - 1 Jan 2020 |

Event | 6th ECCOMAS European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th ECCOMAS European Conference on Computational Fluid Dynamics, ECFD 2018 - Glasgow, United Kingdom Duration: 11 Jun 2018 → 15 Jun 2018 Conference number: 6 |

### Conference

Conference | 6th ECCOMAS European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th ECCOMAS European Conference on Computational Fluid Dynamics, ECFD 2018 |
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Abbreviated title | ECFD 2018 |

Country | United Kingdom |

City | Glasgow |

Period | 11/06/18 → 15/06/18 |

## Keywords

- Deterministic Symbolic Regression
- Explicit Algebraic Reynolds-stress Models
- Machine Learning
- RANS
- Regularised Least-Square Regression
- Turbulence Modelling