A per-instance learned graph-network transfer between coarse and fine discrete-field optimizations improves Darcy and EIT coefficient reconstructions without surrogates or pretraining.
Fullmulti-grid(FMG)algorithms,in:MultigridTechniques:1984GuidewithApplicationstoFluidDynamics, Revised Edition
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ResiPhy-MDNF: A Residual-Based Physics-Aware Multilevel Discrete Neural Field Framework for PDE-Constrained Inverse Problems
A per-instance learned graph-network transfer between coarse and fine discrete-field optimizations improves Darcy and EIT coefficient reconstructions without surrogates or pretraining.