Paper Link: https://doi.org/10.1137/22S1472814
This is the repository for code used in my paper. If you have any questions, you can reach me at andyzhu at cmu dot edu.
Glioblastoma is an aggressive brain tumor with cells that infiltrate and proliferate rapidly into surrounding brain tissue. Recently, physics-informed neural networks (PINNs) have emerged as a novel method in scientific machine learning for solving nonlinear PDEs. Compared to traditional solvers, PINNs leverage unsupervised deep learning methods to minimize residuals across mesh-free domains, enabling greater flexibility while avoiding the need for complex grid constructions.
Here, we implement a general method for solving time-dependent diffusion-reaction PDE models of glioblastoma and inferring biophysical parameters from numerical data via PINNs. We evaluate the PINNs over patient-specific geometries, accounting for individual variations with diffusion mobilities derived from pre-operative MRI scans. Using synthetic data, we demonstrate the performance of our algorithm in patient-specific geometries.
We show that PINNs are capable of solving parameter inference inverse problems in approximately one hour, expediting previous approaches by 20--40 times owing to the robust interpolation capabilities of machine learning algorithms.
