AutoMF: Automated Ensembles for Multifidelity Field Prediction

Learning to predict physical fields requires fine simulation data that can be expensive to obtain. Multifidelity learning combines coarse and fine solutions, but choosing a surrogate from limited fine data can discard useful predictions. We introduce AutoMF, an automated ensemble workflow built around nine surrogates adapted from neural operators, convolutional networks, and reduced basis models. The library includes transfer learning, pooled fidelity training, and coarse field correction, together with a fine only reference. We assemble 22 datasets spanning seven classes of partial differential equations (PDEs) and climate emulation to evaluate whether combining predictions reduces errors in model choice. Five additional input and fine solution pairs, reserved from training, determine one weight per model using established ensemble rules. Weights stay fixed across the field and new inputs, which require no additional coarse simulation. The fitted mixture achieves lower error on 17 of 22 datasets than selecting one model from the same fitting examples. It reduces geometric mean relative $L^2$ error by 11.6% under equal weighting of the seven PDE classes and by 7.5% under equal weighting of all 22 datasets. Against the best implemented baseline chosen separately for each PDE dataset after evaluation, its reduction is 29.4% under equal weighting of the seven PDE classes. Code and reproducibility materials are available in an [anonymous repository](https://anonymous.4open.science/r/mf_field-35BF/README.md).