Turbulence Model Development based on a Novel Method Combining Gene Expression Programming with an Artificial Neural Network Article Swipe
Haochen Li
,
Fabian Waschkowski
,
Yaomin Zhao
,
Richard D. Sandberg
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.07293
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.07293
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 expression programming (GEP) with artificial neural network (ANN), we propose a novel method for symbolic regression called the gene expression programming neural network (GEPNN). In this method, candidate expressions generated by evolutionary algorithms are transformed between the GEP and ANN structures during training iterations, and efficient and robust convergence to accurate models is achieved by combining the GEP's global searching and the ANN's gradient optimization capabilities. In addition, sparsity-enhancing strategies have been introduced to GEPNN to improve the interpretability of the trained models. The GEPNN method has been tested for finding different physical laws, showing improved convergence to models with precise coefficients. Furthermore, for large-eddy simulation of turbulence, the subgrid-scale stress model trained by GEPNN significantly improves the prediction of turbulence statistics and flow structures over traditional models, showing advantages compared to the existing GEP and ANN methods in both a priori and a posteriori tests.
Related Topics
Concepts
Gene expression programming
Artificial neural network
Interpretability
Robustness (evolution)
Genetic programming
Computer science
Artificial intelligence
Machine learning
A priori and a posteriori
Convergence (economics)
Symbolic regression
Data mining
Mathematical optimization
Mathematics
Philosophy
Epistemology
Chemistry
Biochemistry
Economic growth
Economics
Gene
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2301.07293
- https://arxiv.org/pdf/2301.07293
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4317549002
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4317549002Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2301.07293Digital Object Identifier
- Title
-
Turbulence Model Development based on a Novel Method Combining Gene Expression Programming with an Artificial Neural NetworkWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-01-18Full publication date if available
- Authors
-
Haochen Li, Fabian Waschkowski, Yaomin Zhao, Richard D. SandbergList of authors in order
- Landing page
-
https://arxiv.org/abs/2301.07293Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2301.07293Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2301.07293Direct OA link when available
- Concepts
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Gene expression programming, Artificial neural network, Interpretability, Robustness (evolution), Genetic programming, Computer science, Artificial intelligence, Machine learning, A priori and a posteriori, Convergence (economics), Symbolic regression, Data mining, Mathematical optimization, Mathematics, Philosophy, Epistemology, Chemistry, Biochemistry, Economic growth, Economics, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.method, | 50 |
| abstract_inverted_index.methods | 1, 161 |
| abstract_inverted_index.models, | 8, 151 |
| abstract_inverted_index.models. | 106 |
| abstract_inverted_index.network | 30, 46 |
| abstract_inverted_index.precise | 124 |
| abstract_inverted_index.propose | 33 |
| abstract_inverted_index.showing | 118, 152 |
| abstract_inverted_index.trained | 105, 136 |
| abstract_inverted_index.(GEPNN). | 47 |
| abstract_inverted_index.accurate | 74 |
| abstract_inverted_index.achieved | 77 |
| abstract_inverted_index.compared | 154 |
| abstract_inverted_index.existing | 157 |
| abstract_inverted_index.gradient | 87 |
| abstract_inverted_index.improved | 119 |
| abstract_inverted_index.improves | 140 |
| abstract_inverted_index.physical | 7, 116 |
| abstract_inverted_index.symbolic | 38 |
| abstract_inverted_index.training | 66 |
| abstract_inverted_index.addition, | 91 |
| abstract_inverted_index.candidate | 51 |
| abstract_inverted_index.combining | 22, 79 |
| abstract_inverted_index.different | 115 |
| abstract_inverted_index.efficient | 69 |
| abstract_inverted_index.generated | 53 |
| abstract_inverted_index.searching | 83 |
| abstract_inverted_index.advantages | 153 |
| abstract_inverted_index.algorithms | 56 |
| abstract_inverted_index.artificial | 28 |
| abstract_inverted_index.expression | 24, 43 |
| abstract_inverted_index.introduced | 96 |
| abstract_inverted_index.large-eddy | 128 |
| abstract_inverted_index.posteriori | 168 |
| abstract_inverted_index.prediction | 142 |
| abstract_inverted_index.regression | 39 |
| abstract_inverted_index.simulation | 129 |
| abstract_inverted_index.statistics | 145 |
| abstract_inverted_index.strategies | 93 |
| abstract_inverted_index.structures | 64, 148 |
| abstract_inverted_index.turbulence | 144 |
| abstract_inverted_index.Data-driven | 0 |
| abstract_inverted_index.convergence | 72, 120 |
| abstract_inverted_index.expressions | 52 |
| abstract_inverted_index.iterations, | 67 |
| abstract_inverted_index.limitations | 13 |
| abstract_inverted_index.programming | 25, 44 |
| abstract_inverted_index.robustness. | 20 |
| abstract_inverted_index.traditional | 150 |
| abstract_inverted_index.transformed | 58 |
| abstract_inverted_index.turbulence, | 131 |
| abstract_inverted_index.Furthermore, | 126 |
| abstract_inverted_index.evolutionary | 55 |
| abstract_inverted_index.optimization | 88 |
| abstract_inverted_index.performance, | 17 |
| abstract_inverted_index.capabilities. | 89 |
| abstract_inverted_index.coefficients. | 125 |
| abstract_inverted_index.significantly | 139 |
| abstract_inverted_index.subgrid-scale | 133 |
| abstract_inverted_index.generalizability | 18 |
| abstract_inverted_index.interpretability | 102 |
| abstract_inverted_index.sparsity-enhancing | 92 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |