Efficient Machine Learning Models Informed by Multiphysics Simulations of Air-Breathing PEM Fuel Cells

This study presents the first comprehensive machine learning framework for predicting the performance of an air-breathing polymer electrolyte membrane fuel cell, based on high-fidelity multiphysics data and validated under realistic conditions. Using data generated from a validated multiphysics model, four machine learning models are trained: MLR, RFR, ANN, and SVR. The models aim to capture the effects of geometric, material, and operating parameters on cell performance to support the development of more efficient and sustainable clean energy systems. Evaluation with standard error metrics shows that MLR exhibits large deviations from actual values, highlighting the limitations of linear models and underscoring the need for more complex approaches. ANN and SVR provide high predictive accuracy and generalize well to unseen data, while RFR tends to overfit. Robustness analysis using white Gaussian noise and four-fold cross-validation further confirms the reliability of top-performing models. ANN and SVR models generate polarization curves 4000 and 40,000 times faster, respectively, than the multiphysics model, enabling real-time applications. Both models achieved excellent predictive performance, with R2 values exceeding 0.999 under normal operating conditions and remaining above 0.98 even in the presence of noisy inputs.