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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.04175 |
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| _version_ | 1866911785217425408 |
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| author | Peterson, Curtis Taylor Hasenfratz, Anna |
| author_facet | Peterson, Curtis Taylor Hasenfratz, Anna |
| contents | Common to many analysis pipelines in lattice gauge theory and the broader scientific discipline is the need to fit a semi-parametric model to data. We propose a fit method that utilizes a radial basis function network to approximate the non-parametric component of such models. The approximate parametric model is fit to data using the basin hopping global optimization algorithm. Parameter constraints are enforced through Gaussian priors. The viability of our method is tested by examining its use in a finite-size scaling analysis of the $q$-state Potts model and $p$-state clock model with $q=2,3$ and $p=4,\infty$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04175 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Constrained curve fitting for semi-parametric models with radial basis function networks Peterson, Curtis Taylor Hasenfratz, Anna High Energy Physics - Lattice Disordered Systems and Neural Networks Statistical Mechanics Computational Physics Data Analysis, Statistics and Probability Common to many analysis pipelines in lattice gauge theory and the broader scientific discipline is the need to fit a semi-parametric model to data. We propose a fit method that utilizes a radial basis function network to approximate the non-parametric component of such models. The approximate parametric model is fit to data using the basin hopping global optimization algorithm. Parameter constraints are enforced through Gaussian priors. The viability of our method is tested by examining its use in a finite-size scaling analysis of the $q$-state Potts model and $p$-state clock model with $q=2,3$ and $p=4,\infty$. |
| title | Constrained curve fitting for semi-parametric models with radial basis function networks |
| topic | High Energy Physics - Lattice Disordered Systems and Neural Networks Statistical Mechanics Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2402.04175 |