Title: Memory-Augmented Functional Koopmanism for Interpretable Learning of Spatiotemporal Dynamics
Primary area: learning on time series and dynamical systems
Keywords: partial differential equations, Koopman learning, reduced order modeling, non-Markovian, spatiotemporal forecasting

Abstract: Precise prediction of spatiotemporal dynamics over predictive horizons is constrained by the computational cost of high-fidelity solvers and the sparsity, noise, and irregularity of data. We introduce MERLIN, a Koopman-based framework that lifts dynamics to the evolution of learned \textit{observation functionals} with near-linear progression, enabling full-field reconstruction at arbitrary resolutions. Theoretically, we develop a functional Koopman theory for PDEs and compensate for the loss of finite-dimensional linear invariance via the Mori–Zwanzig formalism, which augments the linear backbone with non-Markovian memory terms to improve predictive accuracy. Practically, MERLIN employs discretization-invariant \textit{function encoders} that map partial, irregular observations to observables, and resolution-free \textit{function decoders} that reconstruct states at arbitrary query points. Training under linear constraints yields an interpretable, low-dimensional model that captures principal modes, supports reduced-order modeling, and—augmented with memory correction—delivers stable long-horizon rollouts even in ultra-low-dimensional latent spaces.

TL;DR: A data-driven functional Koopmansim with memory correction is proposed for modeling spatiotemporal process.