DeepONet Cardiovascular Modeling
Sep 1, 2024
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1 min read
Overview
Applied Deep Operator Networks (DeepONet) to cardiovascular hemodynamics, enabling rapid patient-specific blood flow predictions with physics-informed neural network architectures.
Key Features
- Operator learning for parametric cardiovascular flows
- Physics-informed loss functions incorporating Navier-Stokes equations
- Generalization across varying patient geometries
Authors
PhD Candidate
I develop AI-driven non-invasive biomarker prediction systems using
photoplethysmography (PPG) signals, bridging computational fluid dynamics
and deep learning for clinical diagnostics. My recent work on physics-integrated
blood viscosity assessment was published in Computer Methods and Programs
in Biomedicine (2025).