Coupled transport processes like blood flow and oxygen delivery are important for cellular metabolism and organ function in the human body. Nonlinear partial differential equations (PDEs) that describe convection, diffusion, and biochemical reactions control these processes. Conventional numerical methods, including finite difference and finite element techniques, necessitate dense discretization and comprehensive boundary data, thereby constraining their efficacy in realistic biomedical contexts characterized by sparse or noisy measurements. This research suggests a physics-informed deep learning framework for simulating blood flow and oxygen transport in human physiology. The method uses Physics-Informed Neural Networks (PINNs) to put the governing PDEs directly into the training process. This lets the model learn physically consistent solutions without needing a lot of labeled data. The framework combines a simplified hemodynamic model with a convection-diffusion-reaction equation that shows how oxygen moves through and is used by tissues. The suggested method doesn't use any meshes and is data efficient. It can work with irregular domains and incomplete data. It is a flexible tool for simulating the delivery of oxygen in cardiovascular and tissue-level systems. It could also be used for inverse modeling and patient-specific analysis. The results show that the approximations are correct and stable, and they don't rely as much on high-resolution data. This supports advanced uses like digital twins and personalized medicine.
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