相关实验视频
Updated: Jun 22, 2025

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Finite Element Modelling of a Cellular Electric Microenvironment
Published on: May 18, 2021
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基于物理学的神经网络能否击败有限元法?
Tamara G Grossmann1, Urszula Julia Komorowska2, Jonas Latz3
1Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK.
概括
本研究比较了解决部分微分方程 (PDEs) 的数值方法. 没有发现物理信息神经网络 (PINNs) 在准确性和速度方面优于已建立的有限元素方法 (FEM).
科学领域:
- 计算数学 计算数学 计算数学
- 数字分析 数字分析
- 科学计算科学计算
背景情况:
- 部分微分方程 (PDEs) 对于科学现象的建模至关重要.
- 数字方法用于模拟PDE解决方案的近似值.
- 基于物理学的神经网络 (PINNs) 是PDEs的最新深度学习方法.
研究的目的:
- 系统地比较PINNs和有限元法 (FEM) 的性能.
- 在各种 PDE 上评估这两种方法的计算成本和近似精度.
主要方法:
- 解决一维,二维和三维的波桑方程.
- 解决1D艾伦-卡恩和1D/2D半线性施罗丁格方程.
- 使用PINNs和FEM进行数值近似.
主要成果:
- 与PINNs相比,FEM通常实现了更高的准确性和解决时间.
- 在特定的实验设置中,PINNs证明了解决的PDE的更快评估.
- 在这项比较研究中,没有发现PINNs与FEM相比具有显著的优势.
结论:
- 有限元法仍然是解决广泛的PDE的可靠和高效选择.
- 虽然有希望,但基于物理的神经网络需要进一步发展,以始终匹配或超过FEM性能.
- 这项研究强调了在数值方法和科学计算的深度学习方面持续研究的必要性.
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