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In this work, we use the PINNs methodology for estimating reduced-order model parameters and the full velocity field from scatter 2D noisy measurements in the ...
In this work, we use the PINNs methodology for estimating reduced-order model parameters and the full velocity field from scatter 2D noisy measurements in the ...
Aug 3, 2023 · In this work, we use the PINNs methodology for estimating reduced-order model parameters and the full velocity field from scatter 2D noisy measurements in the ...
Jun 9, 2023 · Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information.
Aug 2, 2023 · In this work, we use the PINNs methodology for estimating reduced-order model parameters and the full velocity field from scatter 2D noisy measurements in the ...
This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification.
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Physics-informed neural networks for parameter estimation in blood flow models. https://doi.org/10.1016/j.compbiomed.2024.108706 ·. Journal: Computers in ...
In this study, we applied physics-informed neural networks to model three-dimensional, steady-state blood flows and addressed the practically relevant question ...
Jul 16, 2024 · In this work, we present a novel methodology based on PINNs to accurately estimate patient-specific parameters in three-dimensional soft tissue ...
Jun 19, 2024 · This paper explores a PC-based neural net technique for on-line determination of the blood flow rate. Experimental results justify the ...
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