🤖 AI Summary
This study addresses the challenge of unified modeling of source/sink dynamics, cyclic behavior, and topology-constrained transport in complex dynamical systems. By integrating the continuous theory of Helmholtz–Hodge decomposition with discrete data-driven approaches, the authors propose a structured flow modeling paradigm based on graph vector fields (GVFs) and gradient–curl–harmonic decompositions over simplicial complexes. They develop a multi-level modeling strategy that spans from high expressivity to low computational cost through parameterized conditional models and a simplified Hodge representation. A cross-domain validation and diagnosis–simplification iterative pipeline is further designed to ensure model interpretability while achieving computational efficiency. The framework systematically elucidates the trade-offs among model complexity, interpretability, and predictive performance.
📝 Abstract
Many dynamical systems can be described in terms of structured flows combining source/sink behavior, cyclic dynamics, and topology-constrained transport. These features arise across a wide range of domains, including physical, engineered, and data-driven systems. This work provides a unified perspective on such systems by connecting continuous formulations based on the Helmholtz-Hodge decomposition with discrete and data-driven representations.
We review the recently proposed Graph Vector Field (GVF) framework, which enables a decomposition of complex dynamics into gradient, curl, and harmonic components on simplicial complexes, offering both expressivity and interpretability. We then introduce a hierarchy of alternative modeling approaches, including parametric conditional models, linear graph dynamical systems, and reduced Hodge representations, which trade expressive power for computational tractability and reduced data requirements.
A key contribution of this work is a cross-domain validation strategy that leverages datasets from well-understood physical systems to verify model correctness and assess robustness independently of the target application domain. This approach enables a systematic evaluation of the trade-offs between model complexity, interpretability, and predictive performance.
The resulting framework supports an iterative modeling methodology in which highly expressive models are used as diagnostic tools to identify dominant mechanisms, guiding the construction of simplified models tailored to practical constraints. This work highlights the broad applicability of structured flow modeling and provides a foundation for scalable and interpretable analysis of complex dynamical systems.