物理闭合对机器嗅觉很重要：用于可识别动态气体解混的麦克斯韦 - 斯蒂芬图求解器
Physics Closure Matters for Machine Olfaction: A Maxwell-Stefan Graph Solver for Identifiable Dynamic Gas Unmixing
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中文总结 AI 辅助
研究气体解混的机器嗅觉逆问题，提出基于麦克斯韦 - 斯蒂芬方程的UnMixNet图神经网络求解器，将多物理正向过程嵌入气体解混，提升单气味识别等能力，且推断浓度与真值相符，学习到可转移物理指纹。
中文摘要 AI 辅助
气体解混的机器嗅觉是一个约束不足的逆问题，需从低维、延迟和纠缠的传感器响应推断气体成分。关键障碍之一是物理闭合错误设定。本文将气体解混表述为受麦克斯韦 - 斯蒂芬多组分输运偏微分方程、竞争吸附常微分方程和非线性传感器转导常微分方程约束的多物理逆问题。提出UnMixNet，将多物理正向过程嵌入端到端气体解混。在SmellNet上评估显示单气味识别等能力提升，UCI动态气体混合物外部验证表明推断浓度过程与地面真值相符，模型学习到可转移动态物理指纹，更好满足多种闭合。
英文摘要
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.
