PINO Coronary Flow Prediction
Jun 1, 2024
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1 min read
Overview
Developed a Physics-Informed Neural Operator (PINO) framework for predicting coronary artery blood flow patterns, providing a fast surrogate for computational fluid dynamics (CFD) simulations.
Key Contributions
- Orders of magnitude faster than traditional CFD
- Physics-constrained training for physically consistent results
- Validated against high-fidelity simulation benchmarks
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).