AI for Power Electronics — Algorithm Selector

Open the web app →
_{If the hosted link is unavailable, see Run locally.}

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Authorship & status

• Web app author: Xinze Li
• Tutorial article: Xinze Li, Fanfan Lin, Juan J. Rodríguez-Andina, Sergio Vazquez, Homer Alan Mantooth, Leopoldo García Franquelo, “Fundamentals of Artificial Intelligences for Power Electronics,” IEEE Transactions on Industrial Electronics, 2026.

Under construction — flows, copy, and links may change. Companion course repo: Fundamentals_of_AI_for_PE.

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About

Interactive wizard that recommends AI/ML approaches aligned with the tutorial article and the Fundamentals_of_AI_for_PE notebooks.

ChatGPT companion: For deeper Q&A and report-style help, a button below the wizard and report (above the disclaimer footer) opens the Fundamentals of AI for PE Custom GPT in a new tab.

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Run locally

The app uses ES modules; opening index.html as a file will not work — use a local HTTP server.

Windows: double-click start-server.bat (or run start-server.ps1), then open http://127.0.0.1:8765/.

Command line:

cd AI_for_PE_Algorithm_Selector
python -m http.server 8765

Then open http://127.0.0.1:8765/ in your browser.

If the port is in use, choose another (e.g. 8080) or use:

npx --yes serve -l 8765

Troubleshooting: confirm the terminal shows the server listening; try 127.0.0.1 instead of localhost; allow Python through the firewall if needed; check python --version.

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Repository layout

Path	Role
index.html	Page shell
css/styles.css	Styling
js/data.js	Glossary, recommendations, GitHub URLs
js/app.js	Wizard and report UI
start-server.bat / start-server.ps1	Local server (Windows)

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Notes

• RL: the course ships 7_Reinforcement_Learning/RL_buck_control.ipynb (pedagogical DQN) and 7_Reinforcement_Learning/DDPG_buck_control.ipynb (DDPG, same averaged-buck task). Broader RL at scale still often uses external libraries; the selector links Stable-Baselines3 where relevant.
• PINN: 5_PIML/PINN/pinn_ode.ipynb (Newton cooling ODE) and 5_PIML/PINN/pinn_pde.ipynb (Burgers PDE) follow the same stability-oriented pattern: fixed collocation / IC grids, soft (MSE) constraints, weighted composite losses, Adam with gradient clipping + ReduceLROnPlateau, and optional L-BFGS polish.
• 3D field / thermal maps: 4_Neural_Network/Field_Data/field_temperature_residual_fnn.ipynb trains a residual FNN on downsampled Tfield_* CSVs (x,y,z,T; loss and Tamb from filenames), with per-file train/val/test splits and 3-D residual plots—complements tabular FNN tutorials in the same chapter.
• Some paths (e.g. full GNN training) point to external libraries or papers when the course has no matching notebook.
• Notebook links in the app point to https://github.com/XinzeLee/Fundamentals_of_AI_for_PE/... — keep that repo public or change REPO_ROOT in js/data.js.