DCP-INN combines a diamond-shaped main network for low-frequency flow trends with a parallel correction network for high-frequency residuals, plus a Taylor-expansion high-order loss, to reconstruct hemodynamics accurately from sparse data in tortuous vessels.
Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning
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Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data
DCP-INN combines a diamond-shaped main network for low-frequency flow trends with a parallel correction network for high-frequency residuals, plus a Taylor-expansion high-order loss, to reconstruct hemodynamics accurately from sparse data in tortuous vessels.