Scientific ML · Water & Wastewater
Physics-Informed Neural Networks (PINNs) for Water & Wastewater
Apply the physics-informed neural networks (pinns) to real water & wastewater problems — in the browser, with AI assistance.
PINNs embed PDE residuals in the loss so the network learns solutions consistent with physics from sparse data.
In water & wastewater, teams face challenges like flow assurance, mixing and aeration, contaminant transport. The physics-informed neural networks (pinns) directly supports use cases such as pipe-network hydraulics, aeration mixing, contaminant dispersion, and PolySim's AI Copilot can recommend settings and catch common setup errors before you run.
Typical Water & Wastewater use cases
- Pipe-network hydraulics
- Aeration mixing
- Contaminant dispersion
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