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              <p>&lt;div&gt;&lt;p&gt;This study presents a comprehensive investigation into the estimation of blood flow and cardiovascular parameters using physics-informed neural networks (PINNs) coupled with ensemble learning techniques. Four distinct architectures were investigated and implemented, incorporating various Windkessel models. The Windkessel model is a cardiovascular model linking blood flow to blood pressure and is categorized as a lumped (0D) model. We explored various Windkessel models: the two-and three-element Windkessels, as well as their fractional versions, which include fractionalorder capacitors. A complete physics-informed deep-learning estimation framework is proposed, taking as input blood pressure signals and giving as output estimated blood flow and cardiovascular parameters. Through rigorous training and fine-tuning, robust convergence was achieved, enabling accurate estimation of blood flow and cardiovascular parameters. Using two datasets for validation with various data distributions and patients' clinical conditions, the results demonstrate consistent performance across various cardiac profiles. The transferability of the trained models for unseen data distribution was investigated. Particularly, the introduction of fractional order elements in the Windkessel models exhibited improved accuracy without increasing model complexity. The analysis of parameters' distributions and their physiological coherence further validated the reliability of the obtained estimations. Precisely, we conducted an in-depth study of the estimated peripheral vascular resistance and fractional order, comparing them to their expected values according to the literature. A comparative study between the proposed method and existing literature showed the efficiency of the proposed framework. Finally, the framework is validated using In-Vivo human data to demonstrate its effectiveness on real-world measurements.&lt;/p&gt;&lt;/div&gt;</p>
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