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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/2412.15941 |
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| _version_ | 1866915073659764736 |
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| author | Miranda-Valdez, Isaac Y. Niinistö, Aaro Mäkinen, Tero Lejon, Juha Koivisto, Juha Alava, Mikko J. |
| author_facet | Miranda-Valdez, Isaac Y. Niinistö, Aaro Mäkinen, Tero Lejon, Juha Koivisto, Juha Alava, Mikko J. |
| contents | Mathematical modeling is a powerful tool in rheology, and we present pyRheo, an open-source package for Python designed to streamline the analysis of creep, stress relaxation, oscillation, and rotation tests. pyRheo contains a comprehensive selection of viscoelastic models, including fractional order approaches. It integrates model selection and fitting features and employs machine intelligence to suggest a model to describe a given dataset. The package fits the suggested model or one chosen by the user. An advantage of using pyRheo is that it addresses challenges associated with sensitivity to initial guesses in parameter optimization. It allows the user to iteratively search for the best initial guesses, avoiding convergence to local minima. We discuss the capabilities of pyRheo and compare them to other tools for rheological modeling of biological matter. We demonstrate that pyRheo significantly reduces the computation time required to fit high-performance viscoelastic models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15941 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | pyRheo: An open-source Python package for complex rheology Miranda-Valdez, Isaac Y. Niinistö, Aaro Mäkinen, Tero Lejon, Juha Koivisto, Juha Alava, Mikko J. Soft Condensed Matter Mathematical modeling is a powerful tool in rheology, and we present pyRheo, an open-source package for Python designed to streamline the analysis of creep, stress relaxation, oscillation, and rotation tests. pyRheo contains a comprehensive selection of viscoelastic models, including fractional order approaches. It integrates model selection and fitting features and employs machine intelligence to suggest a model to describe a given dataset. The package fits the suggested model or one chosen by the user. An advantage of using pyRheo is that it addresses challenges associated with sensitivity to initial guesses in parameter optimization. It allows the user to iteratively search for the best initial guesses, avoiding convergence to local minima. We discuss the capabilities of pyRheo and compare them to other tools for rheological modeling of biological matter. We demonstrate that pyRheo significantly reduces the computation time required to fit high-performance viscoelastic models. |
| title | pyRheo: An open-source Python package for complex rheology |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2412.15941 |