Announcing AutoEmulate v1.0: a tool for accelerating large-scale simulations
Open-source ML emulation tooling from the Turing Institute may interest CSIRO and research-adjacent APS agencies using large-scale simulation.
Key points
- The Alan Turing Institute has released AutoEmulate v1.0, an open-source tool for automating creation of simulation emulators.
- Emulators replace expensive large-scale simulations with fast ML surrogates - relevant to scientific and policy modelling use cases.
- Limited direct relevance to APS AI governance or policy work; more applicable to research-intensive agencies.
Summary
The Alan Turing Institute has released AutoEmulate v1.0, an open-source Python package that automates the construction of machine learning emulators for large-scale simulations. Emulators act as fast, accurate surrogates for computationally expensive simulation models, reducing the time and cost of running repeated simulation experiments. The tool targets scientific and engineering workflows where high-fidelity simulations are a bottleneck. Relevance to core APS AI governance and policy work is limited, though agencies with computational modelling functions may find it worth monitoring.
Implications for Australian agencies
- Monitor Research-intensive APS agencies such as CSIRO or Bureau of Meteorology may want to monitor AutoEmulate for applicability to their simulation-heavy workflows.
Implications are AI-generated. Starting points, not advice.
"Announcing AutoEmulate v1.0: a tool for accelerating large-scale simulations" Source: Alan Turing Institute – Blog Published: 31 July 2025 URL: https://www.turing.ac.uk/blog/announcing-autoemulate-v1 The Alan Turing Institute has released AutoEmulate v1.0, an open-source Python package that automates the construction of machine learning emulators for large-scale simulations. Emulators act as fast, accurate surrogates for computationally expensive simulation models, reducing the time and cost of running repeated simulation experiments. The tool targets scientific and engineering workflows where high-fidelity simulations are a bottleneck. Relevance to core APS AI governance and policy work is limited, though agencies with computational modelling functions may find it worth monitoring. Implications for Australian agencies: - [Monitor] Research-intensive APS agencies such as CSIRO or Bureau of Meteorology may want to monitor AutoEmulate for applicability to their simulation-heavy workflows. Retrieved from SIMS, 18 May 2026.