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Physics-constrained graph closures and working-point conditioning for a fast surrogate of linac beam dynamics
When a graph neural network estimates a physical quantity, its frame, edge direction, aggregation and feature normalization decide which laws it can break. Wrong choices leave systematic errors the loss never registers. We build a linac beam-dynamics surrogate on this observation. Space charge, shor
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