A Frobenius-Optimal Projection for Enforcing Linear Conservation in Learned Dynamical Models

• URL: http://arxiv.org/abs/2512.22084v1
• Date: Fri, 26 Dec 2025 17:11:16 GMT
• Title: A Frobenius-Optimal Projection for Enforcing Linear Conservation in Learned Dynamical Models
• Authors: John M. Mango, Ronald Katende,
• Abstract summary: We show that $Astar$ enforces exact conservation while minimally perturbing the dynamics.<n>We prove that $Astar$ enforces exact conservation while minimally perturbing the dynamics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the problem of restoring linear conservation laws in data-driven linear dynamical models. Given a learned operator $\widehat{A}$ and a full-rank constraint matrix $C$ encoding one or more invariants, we show that the matrix closest to $\widehat{A}$ in the Frobenius norm and satisfying $C^\top A = 0$ is the orthogonal projection $A^\star = \widehat{A} - C(C^\top C)^{-1}C^\top \widehat{A}$. This correction is uniquely defined, low rank and fully determined by the violation $C^\top \widehat{A}$. In the single-invariant case it reduces to a rank-one update. We prove that $A^\star$ enforces exact conservation while minimally perturbing the dynamics, and we verify these properties numerically on a Markov-type example. The projection provides an elementary and general mechanism for embedding exact invariants into any learned linear model.

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