Physics Closure Matters for Machine Olfaction: A Maxwell--Stefan Graph Solver for Identifiable Dynamic Gas Unmixing
Machine olfaction for gas unmixing faces a fundamental challenge: inferring gas compositions from low-dimensional, delayed sensor responses. Traditional neural networks often miss physics closure. This paper introduces UnMixNet, a graph neural solver that embeds Maxwell-Stefan multicomponent transport, competitive adsorption, and sensor nonlinearities into the learning process. Tests on SmellNet and UCI dynamic gas mixtures demonstrate improved accuracy and generalization, learning transferable dynamic physical fingerprints.
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[Submitted on 20 Jul 2026]
Title:Physics Closure Matters for Machine Olfaction: A Maxwell--Stefan Graph Solver for Identifiable Dynamic Gas Unmixing
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Abstract:Machine olfaction for gas unmixing is an underconstrained inverse problem in which gas compositions must be inferred from low-dimensional, delayed, and entangled sensor responses produced by interacting chemical transport, surface adsorption, and sensor transduction. One of the key obstacles is physics closure misspecification, where a neural network is designed to fit sensor traces rather than infer a physically closed olfactory process. In this work, we formulate gas unmixing as a multi-physics-constrained inverse problem governed by Maxwell--Stefan multicomponent transport PDEs, competitive adsorption ODEs, and nonlinear sensor transduction ODEs. Directly solving such a high-dimensional coupled system is computationally expensive and often numerically unstable. To this end, we propose UnMixNet, a physics-closed graph neural solver that embeds this multi-physics forward process into end-to-end gas unmixing. UnMixNet discretizes Maxwell--Stefan cross-diffusion on spatial graphs and formulates the multicomponent flux on each edge. This design enables local, differentiable, and flux-conservative inference for multicomponent cross-diffusion. Evaluations on SmellNet show improved single-odor recognition, seen-mixture unmixing, and unseen-mixture generalization. In addition, an external validation on UCI Dynamic Gas Mixtures shows that the inferred concentration process agrees with ground truth concentration set points under dynamic transitions. Process-consistency diagnostics further show that the proposed model learns transferable dynamic physical fingerprints that better satisfies transport, conservation, adsorption, and readout closure.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.18544 [cs.CV]
(or arXiv:2607.18544v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.18544
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yue Shi [view email] [v1] Mon, 20 Jul 2026 22:21:33 UTC (1,197 KB)
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