One post tagged with "generic bioreactor modelling"

这是一篇使用 PINN 用于通用生物反应器建模的论文。

我们先来看看这篇论文的摘要：

摘要​

英文原文：

Many previous studies have explored hybrid semiparametric models merging Artificial Neural Networks (ANNs) with mechanistic models for bioprocess applications. More recently, Physics-Informed Neural Networks (PINNs) have emerged as promising alternatives. Both approaches seek to incorporate prior knowledge in ANN models, thereby decreasing data dependency whilst improving model transparency and generalization capacity. In the case of hybrid semiparametric modelling, the mechanistic equations are hard coded directly into the model structure in interaction with the ANN. In the case of PINNs, the same mechanistic equations must be “learned” by the ANN structure during the training. This study evaluates a dual-ANN PINN structure for generic bioreactor problems that decouples state and reaction kinetics parameterization. Furthermore, the dual-ANN PINN is benchmarked against the general hybrid semiparametric bioreactor model under comparable prior knowledge scenarios across 2 case studies. Our findings show that the dual-ANN PINN can level the prediction accuracy of hybrid semiparametric models for simple problems. However, its performance degrades significantly when applied to extended temporal extrapolation or to complex problems involving high-dimensional process states subject to time-varying control inputs. The latter is primarily due to the more complex multi-objective training of the dual-ANN PINN structure and to physics-based extrapolation errors beyond the training domain.

翻译：

许多先前的研究探索了将人工神经网络（ANN）与机制模型融合的混合半参数模型，用于生物过程应用。近年来，物理信息神经网络（PINN）作为有前景的替代方案应运而生。这两种方法都试图将先验知识融入 ANN 模型，从而降低对数据的依赖性，同时提高模型的透明度和泛化能力。在混合半参数建模中，机理方程被直接硬编码到模型结构中并与 ANN 交互作用；而在 PINN 中，相同机理方程需由 ANN 结构在训练过程中“学习”获得。本研究针对通用生物反应器问题评估了一种双 ANN-PINN 结构，该结构实现了状态参数与反应动力学参数的解耦。此外，通过两个案例研究，在可比先验知识情境下，将双 ANN PINN 模型与通用混合半参数化生物反应器模型进行基准对比。研究发现：对于简单问题，双 ANN-PINN 可达到与混合半参数模型相当的预测精度；但在扩展时间外推或涉及高维过程状态与时变控制输入的复杂问题中，其性能显著下降。后者主要源于双 ANN-PINN 结构更复杂的多目标训练过程，以及训练域外基于物理的外推误差。

通过摘要可以看出，文章利用 PINN 来求解一类同样由微分方程描述的生物反应器系统。这确实展示了 PINN 在传统物理模型之外的又一类应用场景——它同样能够有效处理其他类型的微分方程。至于该方法的具体实现方式，感兴趣的读者可以继续阅读下文以进一步了解。