A twelve-week, tool-agnostic course on how to operate a family of research simulators and the reproducible workflow that turns a simulator run into defensible scientific evidence. The course teaches the class of each tool — an agent-based pedestrian engine, a trajectory-measurement library, a ray-traced channel simulator, and the notebook-as-simulator pattern — rather than any single product, then shows how a knowledge platform (campaigns, sessions, runs, vault notes) makes a sprawl of runs reproducible and citable.
The throughline: a simulation is a scientific instrument. An instrument you cannot reproduce, version, and validate is not evidence — it is an opinion with a plot attached.
The 12-week arc
3 of 12 lectures published — each a self-contained read with a slide deck and runnable workbook. Start at the first published week and work forward.
| Wk | Theme & focus | Read |
|---|---|---|
| 01 | What is a research simulator? start here The four simulator classes; determinism, seeds, the run / artefact / manifest contract | read → slides · workbook |
| 02 | Pedestrian dynamics on the platform Operating an agent-based engine + a measurement library (JuPedSim → PedPy) end-to-end | read → slides · workbook |
| 03 | Ray-traced channel simulation Scenes, emitters, scatterers; synthetic CSI/BLE; occlusion and hardware impairments. Must reflect the IP-119 reparameterisation: occupancy diffuse power is channel-relative Rician K (K_dB(N) = k0 − slope·N, per-link diffuse = \ | read → slides · workbook |
| 04 | The notebook as a simulator Parameterised, papermill-executed notebooks; interactive replays as artefacts | planned |
| 05 | Coupling simulators Crowd → channel co-simulation; data contracts between pipeline stages | planned |
| 06 | Reproducibility & provenance Pinned digests, git SHA, seeds, deterministic artefacts, manifest schema checks | planned |
| 07 | Parameter sweeps & experiment design Grids, factors, seeds, confounds; what a sweep can and cannot tell you | planned |
| 08 | Supervised research campaigns Brief → sessions → runs → synthesis; success criteria; negative results | planned |
| 09 | Artefact stores & cloud runs Object-store layout, signed URLs, fetching and inspecting run artefacts | planned |
| 10 | Validation & the sim-to-real gap Comparing synthetic to measured; metrics; where fidelity breaks | planned |
| 11 | Scaling & orchestration CI dispatch, container isolation, cost and budget control | planned |
| 12 | Capstone Design, run, and synthesise a small reproducible campaign end-to-end | planned |
Capstone
Week 12: pose a research question answerable by simulation, write a one-page brief with explicit success criteria, run a small sweep or campaign on the platform, synthesise the result (including any negative result), and hand in a package a classmate can re-run from the manifest alone.