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一种适应性h精炼方法用于边界元素快速多极方法,用于准静态电磁建模
William A Wartman1, Konstantin Weise2,3, Manas Rachh4
1Electrical and Computer Engineering Department, Worcester Polytechnic Inst., Worcester, MA 01609 United States of America.
Physics in medicine and biology
|February 5, 2024
概括
适应性网状精细化 (AMR) 显著减少了跨电刺激 (TES) 和电脑学 (EEG) 模拟中的建模错误. 这种方法提高了大脑刺激和神经生理记录的准确性.
科学领域:
- 计算神经科学是一种计算神经科学.
- 生物医学工程 生物医学工程
- 电子生理学 电子生理学
背景情况:
- 标准的多间隔头型号可以在电场和电位计算中产生重大错误.
- 这些错误会影响模拟大脑刺激和神经生理记录的准确性.
研究的目的:
- 为了量化跨脑磁刺激 (TMS),跨脑电刺激 (TES) 和电脑电图 (EEG) 的建模错误,前置问题.
- 为了消除这些错误,使用自适应网状精炼 (AMR) 算法.
主要方法:
- 使用快速多极加速的边界元素方法 (BEM-FMM) 开发和研究了一种AMR方法.
- 该AMR方法有效地将额外的计算资源分配给关键模型区域.
- 准确性是从TES,TMS和EEG的人类连接体项目的头部模型上评估的.
主要成果:
- 适应性精炼的解决方案与"银色标准"解决方案显示出了很好的一致性.
- 对准确的TES和EEG建模至关重要,AMR降低了超过60%的平均误差,网状元件增加<25%.
- 经电刺激和脑电图观察到显著的改善.
结论:
- 抗氧化剂反应有效地消除了用于大脑刺激和神经生理记录的头部模型中的大量建模错误.
- 准确的建模对于诸如跨脑电刺激剂量和电脑学场分析等应用至关重要.
- 预计AMR方法将适用于其他用于电磁模拟的数值建模包.
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