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Compute-optimal data scaling for neural surrogates via multi-fidelity training
Paul Setinek, Fabian Paischer, Nils Thuerey +2
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,…
Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statistics
Anna Zimmel, Paul Setinek, Gianluca Galletti +2
Machine learning surrogates are increasingly used in engineering to accelerate costly simulations, yet distribution shifts between training and deployment often cause severe perfor…
Towards Multi-Fidelity Scaling Laws of Neural Surrogates in CFD
Paul Setinek, Gianluca Galletti, Johannes Brandstetter
Scaling laws describe how model performance grows with data, parameters and compute. While large datasets can usually be collected at relatively low cost in domains such as languag…
SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts
Paul Setinek, Gianluca Galletti, Thomas Gross +3
Neural surrogates for Partial Differential Equations (PDEs) often suffer significant performance degradation when evaluated on problem configurations outside their training distrib…