Physics‐Guided Machine Learning for Robust Viscosity Modeling of HAMA / GelMA Hybrid Hydrogels Under Batch Effect

Reliable viscosity prediction of methacrylated hyaluronic acid (HAMA)/methacrylated gelatin (GelMA) hybrid hydrogels is essential for reproducible multi‐batch biofabrication, yet batch effect can severely impair the cross‐batch generalization of data‐driven models. We established two experimentally measured HAMA/GelMA viscosity datasets collected at different time periods and covering different formulation ranges to characterize batch‐effect‐induced distributional shifts and support same‐batch, cross‐batch, and out‐of‐distribution (OOD) evaluation. Baseline machine learning (ML) models achieved high same‐batch predictive performance ( R 2 > 0.90) but degraded markedly under cross‐batch conditions ( R 2 = 0.265–0.772). To address these challenges, we propose a physics‐informed correction strategy integrating data‐ and model‐level physical priors. A physics‐informed data preprocessing (PIDP) strategy filters samples that violate the rheological priors that viscosity increases monotonically with polymer concentration and decreases with temperature. PIDP increased cross‐batch R 2 from 0.713 to 0.892 and reduced RMSE by up to 38.8%. A physics‐informed neural network (PINN) further improved physical plausibility of the predictions. Integrating PIDP‐PINN strategy (IPPS) achieved the best overall performance ( R 2 = 0.898) and reduced OOD errors by 37.7%. The strategy enables more robust and physically consistent hydrogel viscosity prediction for practical biofabrication, providing a transferable route toward reliable modeling and intelligent design of soft polymer materials under realistic experimental variability.