Here we show examples of how to use a tool for Autonomous Calibration and Characterisation via Evolutionary Simulation (ACCES), which uses evolutionary optimisation to efficiently, autonomously determine the calibration parameters of a numerical simulation using only simple experimental measurements.
This is a Python library for autonomously learning simulation parameters from experimental data, from the micro to the macro, that can be used anywhere from laptops to clusters. There are two closely related frameworks:
- CoExSiST: Coupled Experimental-Simulational Study Tool
- ACCES: Autonomous Characterisation and Calibration via Evolutionary Simulation
Github Repository: https://github.com/uob-positron-imaging-centre/ACCES-CoExSiST
Training Materials
The training notebooks used at the event can be downloaded or found on the repository. This utilises a virtual implementation of the GranuTools GranuDrum. The repository also includes links to the binder service where each example can be run on a cloud instance.
Documentation
Full documentation for the library can be found here. This includes instructions on how to install the python library.
