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Physics-Informed Implicit Representations of Equilibrium Network Flows
Kevin D. Smith · Francesco Seccamonte · Ananthram Swami · Francesco Bullo

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #305

Flow networks are ubiquitous in natural and engineered systems, and in order to understand and manage these networks, one must quantify the flow of commodities across their edges. This paper considers the estimation problem of predicting unlabeled edge flows from nodal supply and demand. We propose an implicit neural network layer that incorporates two fundamental physical laws: conservation of mass, and the existence of a constitutive relationship between edge flows and nodal states (e.g., Ohm's law). Computing the edge flows from these two laws is a nonlinear inverse problem, which our layer solves efficiently with a specialized contraction mapping. Using implicit differentiation to compute the solution's gradients, our model is able to learn the constitutive relationship within a semi-supervised framework. We demonstrate that our approach can accurately predict edge flows in several experiments on AC power networks and water distribution systems.

Author Information

Kevin D. Smith (UCSB)
Francesco Seccamonte (University of California, Santa Barbara)
Ananthram Swami (Army Research Laboratory, Adelphi)
Francesco Bullo (UC Santa Barbara)

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