Physics-informed reduced-order modelling with equivariant spectral submanifolds

Abstract:Spectral submanifold (SSM) reduction has emerged as a mathematically principled route to reliable nonlinear reduced-order models, capturing dynamics beyond the reach of linear techniques such as Dynamic Mode Decomposition (DMD). The computation of SSMs, however, remains computationally expensive, particularly for high-dimensional systems. In this work, we introduce equivariant spectral submanifold (eSSM) reduction, a novel extension of the SSM framework that explicitly incorporates symmetries of the full-order model into the reduction process. We establish the mathematical foundations of this approach by showing that SSMs are naturally equivariant submanifolds and that the associated charts and reduced dynamics inherit the appropriate induced group actions. Building on this framework, we develop a novel equivariant SSM reduction algorithm that exploits these symmetries to achieve substantially faster computations while also improving model robustness. We demonstrate the advantages of this approach on several benchmark problems including a test from the Common Task Framework for Science.

Machine Learning (cs.LG); Dynamical Systems (math.DS); Numerical Analysis (math.NA)

arXiv:2608.04239 [cs.LG]

(or arXiv:2608.04239v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.04239 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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