Simulations of physical systems are often slow and need lots of compute, which makes them unpractical for real-world applications like digital twins, or when they have to run thousands of times for sensitivity analyses. The goal of AutoEmulate is to make it easy to replace simulations with fast, accurate emulators. To do this, AutoEmulate automatically fits and compares various emulators, ranging from simple models like Radial Basis Functions and Second Order Polynomials to more complex models like Support Vector Machines, Gaussian Processes and Conditional Neural Processes to find the best emulator for a simulation.
You can find the project documentation here, including installation.
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The AutoEmulate project is run out of the Alan Turing Institute.
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Visit autoemulate.com to learn more.
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We have also published a paper in The Journal of Open Source Software.
Please cite this paper if you use the package in your work:
@article{Stoffel2025, doi = {10.21105/joss.07626}, url = {https://doi.org/10.21105/joss.07626}, year = {2025}, publisher = {The Open Journal}, volume = {10}, number = {107}, pages = {7626}, author = {Martin A. Stoffel and Bryan M. Li and Kalle Westerling and Sophie Arana and Max Balmus and Eric Daub and Steve Niederer}, title = {AutoEmulate: A Python package for semi-automated emulation}, journal = {Journal of Open Source Software} }