相关实验视频
Updated: Sep 15, 2025

07:42
A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
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在SIR流行病模型中,以物理信息为基础的神经网络提供了最佳的疫苗接种计划
Minseok Kim1, Yeongjong Kim2, Yeoneung Kim1
1Department of Applied Artificial Intelligence, SeoulTech, Nowon-gu 01811, Republic of Korea.
Mathematical biosciences and engineering : MBE
|July 18, 2025
概括
这项研究引入了一种深度学习方法,以找到根除传染病的最快方法. 该方法准确计算最佳的疫苗接种策略,并将流行病建模中的错误减少80%.
科学领域:
- 流行病学 流行病学
- 计算科学 计算科学
- 应用数学 应用数学 应用数学
背景情况:
- 数学建模对于理解疾病动态至关重要.
- 控制传染病的传播需要有效的策略.
- 汉密尔顿 - 雅各比 - 贝尔曼 (HJB) 方程在最佳控制问题中出现.
研究的目的:
- 确定传染病的最小根除时间.
- 开发一个高效的计算框架,以在流行病模型中进行最佳控制.
- 通过深度学习来近似解决HJB方程的方法.
主要方法:
- 开发了一个物理信息神经网络 (PINN) 框架.
- 使用PINN来近似解决HJB方程的解决方案,以获得最小根除时间.
- 采用可变缩放方法来提高训练的稳定性和准确性.
- 使用动态编程原则来获得最佳的疫苗接种控制.
主要成果:
- 拟议的无网格框架准确地接近了最小根除时间.
- 与标准方法相比,该方法实现了平均平方剩余误差的80%降低.
- 有效地确定了最佳的疫苗接种控制和切换时间.
- 深度学习框架显示了培训稳定性和趋同性的改善.
结论:
- 开发的深度学习框架有效地解决了流行病建模中的最佳控制问题.
- 这种方法为解决HJB方程提供了一个强大的计算工具.
- 该方法为疾病根除策略的准确性和效率提供了显著的改进.
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