AutoEmulate: A PyTorch tool for end-to-end emulation workflows

Abstract

Computational simulations lie at the heart of modern science and engineering, but they are often slow and computationally costly. A common solution is to use emulators: fast, cheap models trained to approximate the simulator. However, constructing these requires substantial expertise. AutoEmulate is a low-code Python package for emulation workflows, making it easy to replace simulations with fast, accurate emulators. AutoEmulate has now been fully refactored to use PyTorch as a backend, enabling GPU acceleration, automatic differentiation, and seamless integration with the broader PyTorch ecosystem. The toolkit has also been extended with easy-to-use interfaces for common emulation tasks, including model calibration (determining which input values are most likely to have generated real-world observations) and active learning (where simulations are chosen to improve emulator performance at minimal computational cost). Together these updates make AutoEmulate uniquely suited to running performant end-to-end emulation workflows.