Compute-optimal data scaling for neural surrogates via multi-fidelity training
Neural surrogates for Partial Differential Equations (PDEs) are usually trained on synthetic data from numerical simulators. As models grow and scaling laws demand larger datasets, the cost of data generation often outweighs the training cost and becomes the bottleneck in the scaling quest. Unlike data in other domains...
Paul Setinek, F. Paischer, Nils Thuerey et al.
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