psetinek/simshift
SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts
This is a benchmark for researchers and engineers developing AI models to predict outcomes of physical simulations. It helps evaluate how well your 'neural surrogate' models, which are fast AI approximations of complex simulations, perform when the conditions or parameters of the simulation change. You input your AI model and simulation data, and it outputs an evaluation of your model's robustness to these changes, particularly in industrial scenarios like hot rolling or electric motor design.
Use this if you are a researcher or engineer working on AI models for physical simulations and need a standardized way to test your model's performance when simulation parameters or conditions shift.
Not ideal if you are looking for a plug-and-play AI model to run your simulations directly, as this tool is for benchmarking and evaluating such models.
Stars
15
Forks
3
Language
Python
License
MIT
Category
Last pushed
Feb 09, 2026
Commits (30d)
0
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