Achieved O(log N) complexity for multi-scale PDE simulations using anchor-based subdivision - looking for similar work

I've implemented a hierarchical subdivision method for solving PDEs across extreme scale ranges (10⁶¹ orders of magnitude) that achieves logarithmic complexity instead of the typical O(N³). Core idea: Instead of storing absolute values at every scale, compute only deltas (differences) between hierarchical levels using adaptive anchor points. Combined with memoization, this reduces a 10¹⁸³-node problem to ~10⁵ effective nodes. Validation: Tested on 2D Schrödinger equation with harmonic potential. Three grid resolutions (80×48, 120×72, 200×120) show RMSE ratios of 0.984 and 0.988, indicating second-order convergence. External fit to analytical solution gives χ² = 0.004925. Performance: Runs on Intel i5 laptop in ~15 seconds for 200 time steps. Standard finite-difference would require 10¹⁵⁰+ years on same hardware.