论文标题
通过物理知情神经网络对药效动物组织培养室模型模拟，建模和预测
Simulation, Modeling and Prediction of a Pharmacodynamic Animal Tissue Culture Compartment Model by Physical Informed Neural Network
论文作者
论文摘要
细胞培养的隔室模型广泛用于细胞学，药理学，毒理学和其他领域。传统微分方程模型方法可以实现隔室模型的数值模拟，数据建模和预测。同时，随着软件和硬件的开发，物理知情的神经网络（PINN）被广泛用于求解微分方程模型。这项工作模型，模拟和预测基于机器学习框架Pytorch的细胞培养室模型，其中具有16个隐藏层神经网络，包括8个线性层和8个反馈活动层。结果显示，以这种方式，三组分四参数定量药效学模型预测的损失值为0.0004853，通过均方误差（MSE）评估。总而言之，物理知情的神经网络可以作为处理细胞培养室模型的有效工具，并且可以在处理大数据集方面表现更好。
Compartment models of cell culture are widely used in cytology, pharmacology, toxicology and other fields. Numerical simulation, data modeling and prediction of compartment models can be realized by traditional differential equation modeling methods. At the same time, with the development of software and hardware, Physical Informed Neural Network (PINN) is widely used to solve differential equation models. This work models, simulates and predicts the cell culture compartment model based on the machine learning framework PyTorch with an 16 hidden layers neural network, including 8 linear layers and 8 feedback active layers. The results showed a loss value of 0.0004853 for three-component four-parameter quantitative pharmacodynamic model predictions in this way, which is evaluated by Mean Square Error (MSE). In summary, Physical Informed Neural Network can serve as an effective tool to deal with cell culture compartment models and may perform better in dealing with big datasets.
