Data-driven parameter identification for tumor growth models
Abstract
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) [1] and DeepONet [2], 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.
Fields of Study & Subjects
Mathematical Biology Tumor Growth
Model Reduction and Neural Networks
Neural Networks and Applications
Publication details
- Journal / source
- Journal of Computational and Applied Mathematics
- Publisher
- Elsevier BV
- Volume
- 489
- Pages
- 117875–117875
- Publication date
- 11 June 2026
- Language
- EN