RecastNet: Adapting Neural Solvers to Network Topology Changes Without Retraining

Many scientific and engineering applications require repeatedly solving large-scale networked systems under varying operating conditions and topologies, with local component equations coupled through the network. Neural solvers can accelerate these computations, but training them to cover the combinatorially many possible topologies is costly and often impractical. We present Neural Network Recasting (RecastNet), which shifts topology adaptation from training to inference: a neural solver is trained only for solution prediction on a single base topology and analytically recast under unseen topology changes. Our key finding is that the input–output Jacobian of the base-topology neural solver can recover the underlying network Laplacian inverse, allowing low-rank topology changes to be incorporated analytically. This yields zero-shot adaptation to unseen topology changes with only lightweight test-time computation. Experiments on synthetic networks and real-world electric grids show that RecastNet consistently outperforms graph-learning and topology-adaptation baselines under topology changes of increasing severity. Overall, RecastNet offers a new paradigm for topology adaptation through analytical recasting rather than cross-topology training.