
{"id":63112,"date":"2025-04-14T12:00:19","date_gmt":"2025-04-14T12:00:19","guid":{"rendered":""},"modified":"2025-04-14T12:00:19","modified_gmt":"2025-04-14T12:00:19","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-63112","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-63112\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0646\u0645\u0648\u062f\u0627\u0631 \u0622\u06af\u0627\u0647 \u0627\u0632 \u0641\u06cc\u0632\u06cc\u06a9 \u0628\u0631\u0627\u06cc \u062a\u062e\u0645\u06cc\u0646 \u0645\u06cc\u062f\u0627\u0646 \u062c\u0631\u06cc\u0627\u0646 \u062f\u0631 \u0634\u0631\u06cc\u0627\u0646 \u0647\u0627\u06cc \u06a9\u0627\u0631\u0648\u062a\u06cc\u062f"},"content":{"rendered":"<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\n<td>Physics-informed graph neural networks for flow field estimation in carotid arteries<\/td>\n<\/tr>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n<td>\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0646\u0645\u0648\u062f\u0627\u0631 \u0622\u06af\u0627\u0647 \u0627\u0632 \u0641\u06cc\u0632\u06cc\u06a9 \u0628\u0631\u0627\u06cc \u062a\u062e\u0645\u06cc\u0646 \u0645\u06cc\u062f\u0627\u0646 \u062c\u0631\u06cc\u0627\u0646 \u062f\u0631 \u0634\u0631\u06cc\u0627\u0646 \u0647\u0627\u06cc \u06a9\u0627\u0631\u0648\u062a\u06cc\u062f<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\n<td>Julian Suk, Dieuwertje Alblas, Barbara A. Hutten, Albert Wiegman, Christoph Brune, Pim van Ooij, Jelmer M. Wolterink<\/td>\n<\/tr>\n<tr>\n<td>\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\n<td>PDF<\/td>\n<\/tr>\n<tr>\n<td>\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u062a\u062d\u0648\u06cc\u0644\u06cc <\/td>\n<td>\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 <\/td>\n<td>\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u062d\u0648\u0647 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 <\/td>\n<td>\u062f\u0648 \u062a\u0627 \u0633\u0647 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634 (\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc)<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n<td>10<\/td>\n<\/tr>\n<tr>\n<td>\u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0631\u0627\u06cc\u06af\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<td><a href=\"https:\/\/arxiv.org\/pdf\/2408.07110\">\u062f\u0627\u0646\u0644\u0648\u062f \u0645\u0642\u0627\u0644\u0647<\/a><\/td>\n<\/tr>\n<tr>\n<td>\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a  <\/td>\n<td>Quantitative Methods,Machine Learning,Fluid Dynamics,\u0631\u0648\u0634\u0647\u0627\u06cc \u06a9\u0645\u06cc , \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 , \u062f\u06cc\u0646\u0627\u0645\u06cc\u06a9 \u0633\u06cc\u0627\u0644 ,<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a    <\/td>\n<td>Submitted 13 August, 2024; originally announced August 2024. , Comments: Preprint. Under Review<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc    <\/td>\n<td>\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 \u062f\u0631 13 \u0627\u0648\u062a 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0627\u0648\u062a 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f. \u060c \u0646\u0638\u0631\u0627\u062a: preprint.\u062a\u062d\u062a \u0628\u0631\u0631\u0633\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u067e\u0627\u06cc\u06af\u0627\u0647 \u0647\u0627\u06cc \u0639\u0644\u0645\u06cc      <\/td>\n<td>\n            <a href=\"https:\/\/inspirehep.net\/arxiv\/2408.07110\">INSPIRE HEP<\/a><br \/>\n            <br \/>\n            <a href=\"https:\/\/ui.adsabs.harvard.edu\/abs\/arXiv:2408.07110\">NASA ADS<\/a><br \/>\n            <br \/>\n            <a href=\"https:\/\/scholar.google.com\/scholar_lookup?arxiv_id=2408.07110\">Google Scholar<\/a><br \/>\n            <br \/>\n            <a href=\"https:\/\/api.semanticscholar.org\/arXiv:2408.07110\">Semantic Scholar<\/a><br \/>\n            <br \/>\n            <a href=\"https:\/\/arxiv.org\/abs\/2408.07110>arXiv<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n<table class=\"table table-striped table-hover table-primary\">\r\n    <tr>\r\n        <td>\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647  <\/td>\r\n        <td>\u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n    <\/tr>\r\n    <tr>\r\n        <td>\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647  <\/td>\r\n        <td>\u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n    <\/tr>\r\n\t<tr>\r\n        <td>\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647  <\/td>\r\n        <td>\u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n    <\/tr>\r\n\t\t<tr>\r\n        <td>\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627  <\/td>\r\n        <td>\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n    <\/tr>\r\n<\/table>\r\n\r\n\n<h2>\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n<p style=\"direction:ltr;\">Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. Non-invasive, in-vivo measurement of these quantities can only be performed using a select number of modalities that are not widely available, such as 4D flow magnetic resonance imaging (MRI). In this work, we create a surrogate model for hemodynamic flow field estimation, powered by machine learning. We train graph neural networks that include priors about the underlying symmetries and physics, limiting the amount of data required for training. This allows us to train the model using moderately-sized, in-vivo 4D flow MRI datasets, instead of large in-silico datasets obtained by computational fluid dynamics (CFD), as is the current standard. We create an efficient, equivariant neural network by combining the popular PointNet++ architecture with group-steerable layers. To incorporate the physics-informed priors, we derive an efficient discretisation scheme for the involved differential operators. We perform extensive experiments in carotid arteries and show that our model can accurately estimate low-noise hemodynamic flow fields in the carotid artery. Moreover, we show how the learned relation between geometry and hemodynamic quantities transfers to 3D vascular models obtained using a different imaging modality than the training data. This shows that physics-informed graph neural networks can be trained using 4D flow MRI data to estimate blood flow in unseen carotid artery geometries.<\/p>\n<h2>\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n<p>\u0645\u0642\u0627\u062f\u06cc\u0631 \u0647\u0645\u0648\u062f\u06cc\u0646\u0627\u0645\u06cc\u06a9\u06cc \u0639\u0648\u0627\u0645\u0644 \u062e\u0637\u0631 \u0632\u06cc\u0633\u062a \u067e\u0632\u0634\u06a9\u06cc \u0628\u0627 \u0627\u0631\u0632\u0634 \u0628\u0631\u0627\u06cc \u0622\u0633\u06cc\u0628 \u0634\u0646\u0627\u0633\u06cc \u0642\u0644\u0628\u06cc \u0639\u0631\u0648\u0642\u06cc \u0645\u0627\u0646\u0646\u062f \u0622\u062a\u0631\u0648\u0627\u0633\u06a9\u0644\u0631\u0648\u0632 \u0647\u0633\u062a\u0646\u062f.\u0627\u0646\u062f\u0627\u0632\u0647 \u06af\u06cc\u0631\u06cc \u063a\u06cc\u0631 \u062a\u0647\u0627\u062c\u0645\u06cc \u0648 \u062f\u0627\u062e\u0644\u06cc 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\u062a\u0627 \u0628\u0647 \u062c\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0628\u0632\u0631\u06af \u062f\u0631\u0648\u0646 \u0633\u06cc\u0644\u06cc\u06a9\u0648 \u06a9\u0647 \u062a\u0648\u0633\u0637 \u062f\u06cc\u0646\u0627\u0645\u06cc\u06a9 \u0633\u06cc\u0627\u0644 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc (CFD) \u0628\u0647 \u062f\u0633\u062a \u0622\u0645\u062f\u0647 \u060c \u0645\u0627\u0646\u0646\u062f \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0641\u0639\u0644\u06cc \u060c \u0645\u062f\u0644 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc MRI \u062f\u0631 \u0627\u0646\u062f\u0627\u0632\u0647 \u06af\u06cc\u0631\u06cc \u0645\u062a\u0648\u0633\u0637 \u200b\u200b4D 4D \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645.\u0645\u0627 \u0628\u0627 \u062a\u0631\u06a9\u06cc\u0628 \u0645\u0639\u0645\u0627\u0631\u06cc \u0645\u062d\u0628\u0648\u0628 PointNET ++ \u0628\u0627 \u0644\u0627\u06cc\u0647 \u0647\u0627\u06cc \u06af\u0631\u0648\u0647\u06cc \u0628\u0647 \u062d\u0627\u0644\u062a \u06af\u0631\u0648\u0647\u06cc \u060c \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0631\u0622\u0645\u062f \u0648 \u0645\u0639\u062a\u0628\u0631 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.\u0628\u0631\u0627\u06cc \u062a\u0631\u06a9\u06cc\u0628 \u0645\u0642\u062f\u0645\u0627\u062a \u0622\u06af\u0627\u0647 \u0627\u0632 \u0641\u06cc\u0632\u06cc\u06a9 \u060c \u0645\u0627 \u06cc\u06a9 \u0637\u0631\u062d \u062a\u0641\u0633\u06cc\u0631 \u06a9\u0627\u0631\u0622\u0645\u062f \u0628\u0631\u0627\u06cc \u0627\u067e\u0631\u0627\u062a\u0648\u0631\u0647\u0627\u06cc \u062f\u06cc\u0641\u0631\u0627\u0646\u0633\u06cc\u0644 \u062f\u0631\u06af\u06cc\u0631 \u0631\u0627 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0645\u06cc 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\u0639\u0635\u0628\u06cc \u0622\u06af\u0627\u0647 \u0627\u0632 \u0641\u06cc\u0632\u06cc\u06a9 \u0645\u06cc \u062a\u0648\u0627\u0646\u0646\u062f \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc MRI \u062c\u0631\u06cc\u0627\u0646 4D \u0628\u0631\u0627\u06cc \u0628\u0631\u0622\u0648\u0631\u062f \u062c\u0631\u06cc\u0627\u0646 \u062e\u0648\u0646 \u062f\u0631 \u0647\u0646\u062f\u0633\u0647 \u0647\u0627\u06cc \u0634\u0631\u06cc\u0627\u0646 \u06a9\u0627\u0631\u0648\u062a\u06cc\u062f \u063a\u06cc\u0628 \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0634\u0648\u0646\u062f.<\/p>\n\r\n<table class=\"table table-striped table-hover table-primary\">\r\n    <tr>\r\n        <td>\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647  <\/td>\r\n        <td>\u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n    <\/tr>\r\n    <tr>\r\n        <td>\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647  <\/td>\r\n        <td>\u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n    <\/tr>\r\n\t<tr>\r\n        <td>\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647  <\/td>\r\n        <td>\u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n    <\/tr>\r\n\t\t<tr>\r\n        <td>\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627  <\/td>\r\n        <td>\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n    <\/tr>\r\n<\/table>\r\n\r\n\n","protected":false},"excerpt":{"rendered":"<p>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Physics-informed graph neural networks for flow field estimation in carotid arteries \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u062a\u0631\u062c\u0645\u0647 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\u0628\u0631\u0627\u06cc \u062a\u062e\u0645\u06cc\u0646 \u0645\u06cc\u062f\u0627\u0646 \u062c\u0631\u06cc\u0627\u0646 \u062f\u0631 \u0634\u0631\u06cc\u0627\u0646 \u0647\u0627\u06cc \u06a9\u0627\u0631\u0648\u062a\u06cc\u062f - \u0641\u0631\u0648\u0634\u06af\u0627\u0647 \u0627\u06a9\u0633\u067e\u0631\u0633\" \/>\n<meta property=\"og:description\" content=\"\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Physics-informed graph neural networks for flow field estimation in carotid arteries \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u062a\u0631\u062c\u0645\u0647 [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/express24.ir\/d\/product\/\u062a\u0631\u062c\u0645\u0647-\u0641\u0627\u0631\u0633\u06cc-\u0645\u0642\u0627\u0644\u0647-63112\/\" \/>\n<meta property=\"og:site_name\" content=\"\u0641\u0631\u0648\u0634\u06af\u0627\u0647 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