書誌事項
- タイトル別名
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- Stochastic Optimization and Uncertainty Quantification of Coronary Stent using Gaussian Process Model
抄録
<p>Coronary stents are small wire meshed tubes used to help restore blood flow in arteries blocked by a buildup of plague. Understanding the uncertainties in the manufacturing as well as operation of stents are vital in order to ensure performance of the device and more importantly the safety of the patient. By combining Finite Element Method (FEM) and Machine Learning (ML), a robust design to account for these uncertainties is made possible. In this study, the effects of geometry parameters along with operational parameters of the expansion of a Palmaz-Schatz stent model were studied. Simulations were conducted using an FEM model build with COMSOL Multiphysics®, and the results were fed into ML software SmartUQ to build a surrogate model. Stochastic optimizations were performed accounting for uncertainties, and performance were compared to other literatures.</p>
収録刊行物
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- 計算力学講演会講演論文集
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計算力学講演会講演論文集 2023.36 (0), OS-0702-, 2023
一般社団法人 日本機械学会
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キーワード
詳細情報 詳細情報について
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- CRID
- 1390581401103046528
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- ISSN
- 24242799
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可