PhyRes-MDNF: Physics-Coupled Residual GNN Correction for Multilevel Discrete Neural Field Inversion

Coefficient inversion on fine grids under PDE constraints is ill conditioned: sparse observations weakly constrain fine-scale parameters, and direct single-resolution optimization must recover state and coefficient fields across all scales simultaneously.

This causes slow, initialization-sensitive convergence; learned transfer models require offline data and can introduce approximation error into the numerical physics.

We propose PhyRes-MDNF, a fixed-physics multilevel discrete neural field framework.

On each level, a single-level DNF represents the inverse unknowns directly as trainable fields and optimizes the discrete objective.

In the full-space Darcy realization, state fields \(U\) and their shared coefficient field \(K\) are optimized jointly in one fixed-physics inverse process.

Between levels, one zero-initialized PhyRes-GNN jointly performs fixed-stencil prolongation and bounded residual correction to construct an incoming target representation, which a fixed initialization map converts to the next DNF variables.

It is fitted anew from the observations and unchanged numerical model, without offline pretraining or fine-grid truth.

Coarse levels therefore resolve large-scale structure before refined degrees of freedom are introduced, shortening the fine-grid optimization path while retaining the original discrete operator.

Under the same final-grid update budget, the multilevel Darcy realization reduces coefficient and state errors by approximately \(85\%\) and \(90\%\), respectively, demonstrating improved accuracy and final-grid iteration efficiency.

On measured KTC2023 EIT data, the full-\(W\) pipeline improves mean Otsu mIoU by approximately \(3.4\%\) over the official linearized CEM reconstruction and \(16.9\%\) over direct single-level DNF.