amir-cardiolab / cardio-data-driven

Data-driven cardiovascular flow modeling: examples and opportunities

cardio-data-driven

Data-driven cardiovascular flow modeling: examples and opportunities

Codes and data used in the examples presented in the paper:
Data-driven cardiovascular flow modeling: examples and opportunities
Amirhossein Arzani, Scott T. M. Dawson
https://arxiv.org/abs/2010.00131
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Matlab codes:
1-PCA: Principal component analysis
2-rPCA: Robust Principal component analysis
3-CS: Compressed sensing
4-Kalman: Data assimlation with Kalman filter
5-POD-DMD: Proper orthogonal decomposition and dynamic mode decomposition
6-ML-ROM-ex1: Machine learning reduced-order models (example 1: Reconstruct high-resolution Womersley
flow with uncertain Womersley number and optimal sensor placement)
6-ML-ROM-ex2: Machine learning reduced-order models (example 2: Reconstruct high-resolution cerebral aneurysm flow with uncertain viscosity)
7-Low-rank: Low-rank data recovery from random spatiotemporal measurements (matrix completion)
8-SINDy-ex1: Sparse identification of nonlinear dynamics (SINDy) (example 1: Discover a blood coagulation and thrombosis model)
8-SINDy-ex2: Sparse identification of nonlinear dynamics (SINDy) (example 2: Discover analytical velocity in transient vortical flows)

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Installation:

Some of the Matlab codes require the following packages to be installed in Matlab before running the code:

CVX: Matlab Software for Disciplined Convex Programming
http://cvxr.com/cvx/

Sparco Toolbox:
http://www.cs.ubc.ca/labs/scl/sparco/

Spot-master
%https://github.com/mpf/spot

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Data:

The data needed by the codes are placed under the /data_rsif_paper folder.
The larger datasets are provided under in this Google Drive link and should be placed under the same /data_rsif_paper folder.

https://drive.google.com/drive/folders/1G5ciEmUSO8sjbqP5DchqTxQU0OXxfRKn?usp=sharing