Data-Driven Parameter Identification for Tumor Growth Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911735358685184 |
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| author | Liu, Liu Wang, Yifei Xu, Qinyu Xu, Xiaoqian |
| author_facet | Liu, Liu Wang, Yifei Xu, Qinyu Xu, Xiaoqian |
| contents | Modeling tumor growth accurately is essential for understanding cancer progression and informing treatment strategies. To estimate the parameters in the tumor growth model described by a nonlinear PDE, we adopt Physics-Informed Neural Networks (PINNs) and DeepONet, which show advantages especially when the observation data is scarce and contains noise. With the help of real-life lab data, we have demonstrated the potential of applying deep learning tools to address data-driven modeling for tumor growth in biology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Parameter Identification for Tumor Growth Models Liu, Liu Wang, Yifei Xu, Qinyu Xu, Xiaoqian Analysis of PDEs Modeling tumor growth accurately is essential for understanding cancer progression and informing treatment strategies. To estimate the parameters in the tumor growth model described by a nonlinear PDE, we adopt Physics-Informed Neural Networks (PINNs) and DeepONet, which show advantages especially when the observation data is scarce and contains noise. With the help of real-life lab data, we have demonstrated the potential of applying deep learning tools to address data-driven modeling for tumor growth in biology. |
| title | Data-Driven Parameter Identification for Tumor Growth Models |
| topic | Analysis of PDEs |
| url | https://arxiv.org/abs/2511.15940 |