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New method reconstructs hidden interaction networks from steady states

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a variational physics-informed ansatz to reconstruct hidden interaction networks from steady-state observations. This method represents the unknown operator as a trainable object and minimizes steady-state residuals across experiments. In specific settings, the stacked equilibrium equations provide explicit conditions for unique recovery, determined by the rank of a compatibility matrix after accounting for experimental gauge freedom. Synthetic benchmarks demonstrate the effectiveness of this approach in discriminating structures using only equilibrium data when governing dynamics are known and node-level equilibria are fully observed. AI

RANK_REASON The cluster contains a research paper detailing a new method for reconstructing interaction networks. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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New method reconstructs hidden interaction networks from steady states

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The cluster contains a research paper detailing a new method for reconstructing interaction networks. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kaiming Luo ·

    Variational Physics-Informed Ansatz for Reconstructing Hidden Interaction Networks from Steady States

    arXiv:2512.13708v2 Announce Type: replace Abstract: Inferring interaction structure from steady-state observations is a central inverse problem when transient trajectories are unavailable. Here we formulate this problem as simultaneous compatibility of a single interaction operat…

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