Reconstruction of instantaneous flow fields from transient velocity snapshots using physics-informed neural networks (Applications to pulsatile blood flow behind a stenosis)

Physics-informed neural networks (PINNs) offer a promising framework by embedding partial differential equations (PDEs) into the loss function together with measurement data, making them well-suited for inverse problems. However, plain PINNs face challenges with time-dependent PDEs due to the high c...

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Príomhchruthaitheoirí:	Kakeru UEDA, Hiro WAKIMURA, Satoshi II
Formáid:	Artigo
Teanga:	Inglês
Foilsithe / Cruthaithe:	The Japan Society of Mechanical Engineers 2026-05-01
Sraith:	Journal of Biomechanical Science and Engineering
Ábhair:	physics-informed neural networks (pinns) magnetic resonance imaging (mri) inverse problem incompressible navier-stokes equations acceleration term pressure gradient transient flow blood flow stenosis
Rochtain ar líne:	https://www.jstage.jst.go.jp/article/jbse/21/2/21_26-00093/_pdf/-char/en
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