Controlled benchmarks show INR architectures differ in whether weight reuse is source-specific or generic, with no architecture dominating all PDE and analytic cases.
Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media
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A finite-element variational inference method delivers full-covariance Bayesian field reconstruction at dimensions exceeding 400,000 for 3D porous media flow using sparse precision parameterization from SPDE priors.
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Architecture Shapes Transfer Specificity in Implicit Neural Representations
Controlled benchmarks show INR architectures differ in whether weight reuse is source-specific or generic, with no architecture dominating all PDE and analytic cases.