Fig. 3: Parity plots for unseen viscosity samples.
From: A physics-enforced neural network to predict polymer melt viscosity
Parity plots are used to assess the models' overall predictive capabilities in new physical regimes based on the physical variable split for molecular weight (Mw), shear rate (\({\dot{\gamma }}\)), and temperature T. Results are compared between Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Physics Enforced Neural Network (PENN) models. Each plot compares experimental values for melt viscosity (η) to the predicted η across 3 unique test-train splits for each physical variable. The top row (A–C) contains GPR results for (A) the Mw split, (B) the \({\dot{\gamma }}\) split (C) the T split. The middle row (D–F) contains ANN results for (D) the Mw split, (E) the \({\dot{\gamma }}\) split (F) the T split. The bottom row (G–I) contains PENN results for (G) the Mw split, (H) the \({\dot{\gamma }}\) split I) the T split. The dotted black lines represent perfect predictions. The coefficient of determination (R2) and Order of Magnitude Error (OME) are reported over these test sets.
