Physics-Informed Neural Network-Based Pulsatile Flow Modeling and Targeted Drug Delivery Optimization in Computational Hemodynamics

PINNs offer a robust, data-efficient approach for simulating vascular dynamics and optimizing personalized drug delivery strategies.

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Publication Details

Journal
Journal of Pharmacy And Bioallied Sciences
Published
2025-12-01
DOI
https://doi.org/10.4103/jpbs.jpbs_1424_25
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

Physics-Informed Neural Network-Based Pulsatile Flow Modeling and Targeted Drug Delivery Optimization in Computational Hemodynamics

Aaryasinh Kunalsinh Vaghela
Journal of Pharmacy And Bioallied Sciences
Model Reduction and Neural Networks
article

Physics-Informed Neural Network-Based Pulsatile Flow Modeling and Targeted Drug Delivery Optimization in Computational Hemodynamics

Aaryasinh Kunalsinh Vaghela
article en

Abstract

PINNs offer a robust, data-efficient approach for simulating vascular dynamics and optimizing personalized drug delivery strategies.

Journal of Pharmacy And Bioallied SciencesVol. 17(Suppl 4)
Applied Coherent Technology (United States) (US)
Openalex Percentile: Top 67%
Model Reduction and Neural Networks
DOIOpen Access PDFOpenAlex
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Physics-Informed Neural Network-Based Pulsatile Flow Modeling and Targeted Drug Delivery Optimization in Computational Hemodynamics — Aaryasinh Kunalsinh Vaghela · Journal of Pharmacy And Bioallied Sciences (2025) | TGRS Research Map | TGRS