A comparison study of supervised learning techniques for the approximation of high dimensional functions and feedback control
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913221260083200 |
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| author | Oster, Mathias Saluzzi, Luca Wenzel, Tizian |
| author_facet | Oster, Mathias Saluzzi, Luca Wenzel, Tizian |
| contents | Approximation of high dimensional functions is in the focus of machine learning and data-based scientific computing. In many applications, empirical risk minimisation techniques over nonlinear model classes are employed. Neural networks, kernel methods and tensor decomposition techniques are among the most popular model classes. We provide a numerical study comparing the performance of these methods on various high-dimensional functions with focus on optimal control problems, where the collection of the dataset is based on the application of the State-Dependent Riccati Equation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01402 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A comparison study of supervised learning techniques for the approximation of high dimensional functions and feedback control Oster, Mathias Saluzzi, Luca Wenzel, Tizian Numerical Analysis Optimization and Control Approximation of high dimensional functions is in the focus of machine learning and data-based scientific computing. In many applications, empirical risk minimisation techniques over nonlinear model classes are employed. Neural networks, kernel methods and tensor decomposition techniques are among the most popular model classes. We provide a numerical study comparing the performance of these methods on various high-dimensional functions with focus on optimal control problems, where the collection of the dataset is based on the application of the State-Dependent Riccati Equation. |
| title | A comparison study of supervised learning techniques for the approximation of high dimensional functions and feedback control |
| topic | Numerical Analysis Optimization and Control |
| url | https://arxiv.org/abs/2402.01402 |