Building high accuracy emulators for scientific simulations with deep neural architecture search

Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully emulates simulations in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.

Automated summary

Computer simulations are important for scientific discovery, but their accuracy is often limited by slow execution. To accelerate simulations, researchers propose using machine learning to build fast emulators, but obtaining large training datasets can be expensive. A new method based on neural architecture search is presented, which can build accurate emulators even with limited training data. The method is successfully applied in various scientific fields and provides uncertainty estimation for emulators.