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    Physics-Informed Neural Networks for Heat Transfer Problems 

    Source: Journal of Heat Transfer:;2021:;volume( 143 ):;issue: 006:;page 060801-1
    Author(s): Cai, Shengze; Wang, Zhicheng; Wang, Sifan; Perdikaris, Paris; Karniadakis, George Em
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Physics-informed neural networks (PINNs) have gained popularity across different engineering fields due to their effectiveness in solving realistic problems with noisy data and often partially missing physics. In PINNs, ...
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    Feasibility of Vascular Parameter Estimation for Assessing Hypertensive Pregnancy Disorders 

    Source: Journal of Biomechanical Engineering:;2022:;volume( 144 ):;issue: 012:;page 121011
    Author(s): Kissas, Georgios;Hwuang, Eileen;Thompson, Elizabeth W.;Schwartz, Nadav;Detre, John A.;Witschey, Walter R.;Perdikaris, Paris
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Hypertensive pregnancy disorders (HPDs), such as preeclampsia, are leading sources of both maternal and fetal morbidity in pregnancy. Noninvasive imaging, such as ultrasound (US) and magnetic resonance imaging (MRI), is ...
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