Machine learning-enhanced image-based blood flow modeling

This talk summarizes some of the recent advances in scientific machine learning and their applications in image-based cardiovascular fluid mechanics. Broadly, we will overview two classes of methods: sparse data-driven modeling and deep learning. We will discuss our recent work on different variants of deep learning such as autoencoders and physics-informed neural networks (PINN) as well as different sparse modeling approaches for super-resolution and denoising of 4D flow magnetic resonance imaging (MRI) data. We will also discuss challenges associated with using deep learning for robust surrogate modeling in patient-specific applications. We will specifically focus on interpretability and generalization issues and present a new explainable AI (XAI) approach based on functional data analysis for interpreting black-box deep learning models and enhancing extrapolation to unseen data.