Tumor Modeling and Parameter Inference with Physics-Informed Neural Networks

This project focuses on using Physics-Informed Neural Networks (PINNs) to accurately estimate key biological parameters and processes in tumor dynamics. By integrating differential equations into neural network training, the approach ensures biologically consistent modeling of tumor growth and treatment response. This is a new approach to simulate disease progression and supports the development of personalized therapies.