Reproducible PINN Framework for Patient-Specific Modeling and Biophysical Parameter Inference in Glioblastoma

Cancer remains a major global health challenge, motivating personalized treatments. PDE-based models can capture tumor dynamics and enable patient-specific predictions, but traditional solvers like FDM or FEM can be computationally costly and require extensive calibration, while purely data-driven neural networks often lack interpretability. Physics-Informed Neural Networks (PINNs) address these limitations by embedding PDE constraints, supporting both forward simulations and inverse parameter estimation. We model glioblastoma growth using a reproducible, open-source PINN framework based on the Fisher–KPP equation. A systematic hyperparameter study evaluates architecture, activation functions, optimizers, learning rates, batching, and sampling strategies. Experiments on synthetic tumors show accurate dynamics and reliable recovery of biophysical parameters. We further provide a standalone Python implementation, transparent datasets, and practical guidelines for reproducible research in personalized oncology.