Tensor Learning: Data-Driven Recovery of the (D, C, P) Decomposition and PDE Solvability Class
Mathematics verified. Core theorems are machine-checked in Lean 4.
Prose and presentation may not have been human-reviewed.
Summary
We propose a hybrid algorithm that recovers the structural $(D, C, P)$
decomposition of a nonlinear PDE system from time-series observations and,
from this decomposition, predicts the solvability class and the
difficulty parameter $\mathcal{D} = \lVe
Length
4,277 words
Claims
6 theorems
Status
Draft
Target
SIAM Journal on Scientific Computing