D7.10 — Summary of Completed Demonstrators

Deliverable D7.10 summarizes all seven NAIC Work Package 7 demonstrator projects, covering their scientific contributions, methodology, infrastructure requirements, and current status.

Tutorial: https://naicno.github.io/wp7-D710-deliverable-report/

Use Cases

UC	Title	Repository	Tutorial	Status
UC1	Climate Indices Teleconnection	wp7-UC1-climate-indices-teleconnection	Tutorial	Completed
UC2	PEM Electrolyzer PINN Optimizer	wp7-UC2-pem-electrolyzer-digital-twin	Tutorial	Completed
UC3	Pseudo-Hamiltonian Neural Networks	wp7-UC3-pseudo-hamiltonian-neural-networks	Tutorial	Completed
UC4	3D Medical Image Registration	wp7-UC4-medical-image-registration	Tutorial	Completed
UC5	Graph-Based AIS Classification	wp7-UC5-ais-classification-gnn	Tutorial	Completed
UC6	Multi-Modal Optimization	wp7-UC6-multimodal-optimization	Tutorial	Completed
UC7	Latent Representation of PDE Solutions	wp7-UC7-latent-pde-representation	Tutorial	Completed

Key Findings

• Physics-informed ML generalizes better with fewer parameters — UC2's 12-parameter student beats ~50K-parameter Transformers on out-of-distribution data by 5–6x
• Data representation matters as much as model choice — UC5's graph representation of vessel trajectories outperforms flat time series regardless of GNN architecture
• NAIC infrastructure bridges interactive and batch computing — UC1 provides both interactive notebooks and CLI parameter sweeps (42,613 experiments)

Repository Structure

D7.10-report.md          # Full deliverable report
content/                  # Sphinx-lesson tutorial documentation
  conf.py                 # Sphinx configuration
  index.rst               # Table of contents
  episodes/               # 12 tutorial episodes (UC1-UC7 + getting started + FAQ)
  images/                 # Logos and diagrams
  downloads/              # Quick reference page
tests/                    # 178 pytest tests (structure, report, episodes)
requirements-test.txt     # CI test dependencies
requirements-docs.txt     # Sphinx documentation dependencies
.github/workflows/        # CI/CD pipelines (test + GitHub Pages deploy)

Documentation

The Sphinx-lesson tutorial covers:

1. Getting Started — Introduction, VM provisioning, quick start for any UC
2. Use Cases — Detailed walkthroughs for all 7 demonstrators (problem, approach, results, quick start)
3. Cross-Cutting Themes — Physics-informed ML, data representation, reproducibility, infrastructure patterns
4. Reference — FAQ, quick clone commands, useful links

Running Tests

pip install -r requirements-test.txt
pytest tests/ -v

Building Documentation Locally

pip install -r requirements-docs.txt
sphinx-build -b html content build/html
# Open build/html/index.html in your browser

Contributors

Institution	Contributors
NORCE Research	Klaus Johannsen, Odd Helge Otterå, Adrian Evensen, Hasan Asyari Arief, Xue-Cheng Tai, Gro Fonnes, Nadine Goris, Bjørnar Jensen, Jerry Tjiputra, Yngve Heggelund
SINTEF Digital	Sølve Eidnes, Kjetil Olsen Lye
UiB	Saruar Alam

License

• Content (tutorials, documentation): CC BY-NC 4.0
• Code: GPL-3.0-only