Introduction to the challenges of simulation for generating realistic data in supervised AI, and the key tools used to model complete experimental setups.
- Overview of the PaNRAID approach: combining multi-scale material simulations with digital twins of experimental facilities
- Introduction to McStas and McXtrace as the primary simulation frameworks
- Motivation for synthetic data generation in supervised learning contexts
- Survey of experimental artefacts and instrumental effects that must be captured in realistic datasets
| Tool | Description | Link |
|---|---|---|
| McStas | Monte Carlo neutron ray-tracing simulation package | https://mcstas.org/ |
| McXtrace | Monte Carlo X-ray tracing simulation package | https://mcxtrace.org/ |
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