[An improved physics-informed neural network with scheduled sampling for modeling the cell culture process for...

Physics-informed neural networks (PINNs) integrate mechanistic equations into neural networks to enhance the predictive accuracy of models, and has found initial application in cell-culture modeling. While PINNs reduce data dependency by constraining the solution space with physical laws, it can still converge to suboptimal solutions in small-sample scenarios. This paper proposes an improved hybrid-physics-informed neural network with scheduled sampling (SS-IHPINN) to build a more robust model for the cell culture processes for producing monoclonal antibodies through physics-guided initialization and scheduled sampling. The proposed model was evaluated and compared through comparative and ablation experiments, which demonstrated that the two functional modules worked synergistically to endow SS-IHPINN with higher predictive performance. Even with variations in cell culture process parameters, the framework exhibits good generalization and transfer learning capabilities. The method proposed in this study effectively enhances modeling performance under small-sample conditions, providing reliable technical support for bioprocess monitoring.