液体里埃潜态动力学 网络用于基于GPU的快速数值模拟,用于计算心脏病学
Matteo Salvador1, Alison Lesley Marsden2
1Institute for Computational and Mathematical Engineering, Stanford University, CA, USA; Cardiovascular Institute, Stanford University, CA, USA; Pediatric Cardiology, Stanford University, CA, USA; Pasteur Labs, Brooklyn, NY 11205, USA.
Computers in biology and medicine
|December 3, 2025
概括
液体里埃LDNets (LFLDNets) 提供了一种具有成本效益的方法来建模复杂的系统. 这些科学机器学习模型有效地为微分方程创建精确的替代模型,优于传统方法.
科学领域:
- 计算科学与工程 计算科学与工程
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 科学机器学习 (ML) 正在成为传统基于物理的数值解析器的有效替代品.
- 当前的ML方法为普通微分方程 (ODE) 和部分微分方程 (PDE) 构建替代模型.
- 需要先进的机器学习模型,能够处理复杂几何学的多尺度,多物理问题.
研究的目的:
- 引入液体里埃LDNets (LFLDNets) 作为潜伏动态网络 (LDNets) 的延伸.
- 为高度非线性微分方程开发参数化时空替代模型.
- 评估LFLDNets在计算心脏病应用中的表现.
主要方法:
- LFLDNets使用一种神经学启发的,稀疏的,液态的神经网络来进行时间动态.
- 使用可调节内核的富里埃嵌入用于改进高频函数的学习.
- 这些模型应用于心脏电生理学和心血管血液动力学中的3D测试案例.
主要成果:
- 与神经ODEs相比,LFLDNets在调节参数,准确性和效率方面表现出卓越的性能.
- 这些模型有效地捕捉了复杂的时空动态和高频函数.
- 基于AI的数值模拟在几分钟内在GPU上执行.
结论:
- LFLDNets提供了一个强大而有效的工具,用于创建复杂微分方程的替代模型.
- 这一进步有助于开发出基于物理知识的数字双胞胎.
- 这种方法对计算心脏病学及其他领域的应用非常有前途.
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