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Key Concepts to Memorize
Water system components (4 stages):
1. Collection
2. Purification
3. Transmission
4. Distribution
Network topology types:
- Branching network — tree structure, dead ends (no redundancy)
- Gridiron network — looped/ring structure (redundancy, more resilient)
Monitoring devices in WDN:
- Flow and pressure sensors
- Water quality sensors
- Noise loggers (for leak detection)
- Smart meters (consumption measurement)
Pure AI models — limitations:
- Can produce non-physically correct results (violate conservation laws)
- Sensitive to noise
- "Black box" — hard to debug
- Cannot guarantee physical plausibility
Pure Physical models — limitations:
- Equations are approximations
- Sensitive to missing or wrong GIS data
- Assume constant material properties
- Difficult to update in real time
Hybrid approach — 2 strategies:
1. Physics for AI (PINN/PIGNN): embed physics equations into the neural network's loss function → model is constrained to follow physical laws during training
2. AI for Physics: use AI to discover missing GIS data, predict discrepancies between model and measurement, or estimate physical properties (e.g., temperature)
Physics Informed Graph Neural Networks (PIGNN):
- Water networks modelled as graphs: pipes = edges, junctions/nodes = nodes
- Message Passing (2 steps):
- Aggregate: each node collects information from neighbouring nodes
- Combine: each node updates its own representation using aggregated info
- Architecture: Encoder → Processor → Decoder
- Physics constraints (PDE loss) embedded in training loss → ensures physical consistency