基于物理学的神经网络用于模拟猪的食后血氨基酸动力学
Zhangcheng Li1, Jincheng Wen1, Zixiang Ren1
1Laboratory for Bio-Feed and Molecular Nutrition, College of Animal Science and Technology, Southwest University, Chongqing 400715, China.
Animals : an open access journal from MDPI
|February 27, 2026
概括
基于物理学的神经网络 (PINNs) 为分析猪氨基酸 (AA) 动力学提供了强大的方法. 这种深度学习方法在数据受限的场景中表现出色,优于传统的非线性最小平方 (NLS) 方法.
科学领域:
- 动物生理学 动物生理学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 餐后血氨基酸 (AA) 动力学对于评估猪的消化效率和代谢健康至关重要.
- 使用非线性最小平方 (NLS) 的常规动力学分析需要频繁取血样和准确的初始参数估计.
研究的目的:
- 开发和评估物理信息神经网络 (PINN) 框架,用于模拟猪AA动力学.
- 将PINN的性能与传统的NLS方法进行比较,特别是在数据有限的条件下.
主要方法:
- 开发了一个PINN框架,将机械常规微分方程 (ODE) 集成到深度学习损失函数中.
- 追溯分析基准数据,模拟密集和稀疏的血液采样策略.
- 将PINN性能与NLS在稳定性,准确性和参数识别稳定性方面进行比较.
主要成果:
- 在密集采样下,PINN表现出高保真度,与NLS相比.
- 在稀疏采样下,PINN显示出卓越的稳定性和预测准确性,显著降低了氨酸和氨酸的根平均平方误差 (RMSE).
- 与NLS相比,PINN表现出增强的参数识别稳定性和预测一致性,克服了对初始猜测的敏感性.
结论:
- PINN框架提供了一个可靠和一致的替代方案,用于模拟猪的AA动态.
- 由于PINN能够将物理定律作为规范化整合在一起,因此即使数据有限,也能够实现强大的反向问题解决.
- 未来的应用可能涉及使用PINNs优化稀疏采样重建精确的生理轨迹.
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