Tangent-Space Multiscale Manifold Methods for Nonlinear Elliptic Problems

Abstract

We introduce a tangent-space multiscale manifold method for nonlinear heterogeneous elliptic problems. The method represents the fine-scale solution by a nonlinear reconstruction of a coarse state. The ideal reconstruction eliminates fine scales through a constrained variational problem, and the computable reconstruction approximates this map by localized nonlinear patch solves blended with a partition of unity. Because the approximation set is a nonlinear manifold, the coarse equation is posed with tangent multiscale test functions. We also formulate a network-interpolated variant in which only the restricted patch outputs used by the partition-of-unity blend, together with their tangent actions, are approximated by local learned maps. For heterogeneous monotone nonlinear diffusion, we record the structural monotonicity, differentiability, patch-map regularity, and conditional perturbation estimates that separate the geometric stability mechanism from localization, residual, and optional learning defects. A rigorous a priori theory for the decay of the localization defect, and the resulting convergence rates in the coarse mesh size, is deferred to a separate analysis; here these defects are controlled conditionally and their decay is demonstrated numerically.

Disclosure

“Use of generative AI. Generative AI tools, including OpenAI’s ChatGPT and An- thropic’s Claude, were used to assist with language editing, LATEX formatting, figure- caption drafting, reference suggestions, and code generation. All AI-assisted material was critically reviewed”

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Classification

Drafting limited passages

Multiplier

5

Verified

2026-08-23T18:53:49.615500Z

Structural counts