发表机构
University of Illinois Urbana-Champaign; Amazon; Sandia National Laboratories（伊利诺伊大学厄巴纳-香槟分校; 亚马逊; 桑迪亚国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在准确表示大气气溶胶群体，提出AeroMELD框架构建低维潜在变量以保留其数学结构，通过尺度-形状分解等实现，能准确重建多种分布及行为，为在潜在空间学习气溶胶过程演化奠定基础。
AI 中文摘要
准确表示大气气溶胶群体对于模拟气溶胶-云相互作用、辐射强迫和冰核形成至关重要，但现有简化方案存在结构假设，限制了捕捉成分多样性和混合状态的能力。机器学习方法提供了更灵活的表示，但标准自动编码器无法保留气溶胶群体的数学结构。我们引入AeroMELD，一个构建保留此结构的低维潜在变量的数学基础框架。我们表明任何置换不变线性编码器必须进行尺度-形状分解，总数量浓度明确表示，潜在形状由每个粒子嵌入的重心组合给出。通过将非线性聚合后阶段转移到学习的诊断映射中同时保留潜在线性，这种聚合潜在状态保留了深度集模型的诊断表现力。使用粒子解析数据作为地面真值，我们直接编码加权粒子群体而非分箱气溶胶状态；尺寸分辨的质量和数量分布仅作为诊断目标和视觉总结。潜在空间准确重建这些分布、CCN光谱、光学系数和无浸入冻结行为，同时保留混合ML-物理模型所需的线性群体结构。尽管实验专注于诊断重建，但嵌入设计使得排放和混合能够被精确表示，并且非线性微物理过程能够在可控的潜在空间中学习。这项工作为直接在潜在空间中学习气溶胶过程演化奠定了基础。
英文摘要
Accurately representing atmospheric aerosol populations is essential for simulating aerosol-cloud interactions, radiative forcing, and ice nucleation, yet existing reduced schemes impose structural assumptions that limit their ability to capture composition diversity and mixing state. Machine-learning approaches offer more flexible representations, but standard autoencoders do not preserve the mathematical structure of aerosol populations and therefore cannot support physically meaningful process operators. We introduce AeroMELD (Aerosol Measure Embedding for Latent Dynamics), a mathematically grounded framework for constructing low-dimensional latent variables that retain this structure. We show that any permutation-invariant linear encoder must take a scale-shape decomposition, with total number concentration represented explicitly and latent shape given by a barycentric combination of per-particle embeddings. This aggregated latent state retains the diagnostic expressiveness of a Deep Sets model by moving the nonlinear post-aggregation stage into the learned diagnostic map while preserving latent linearity. Using particle-resolved data as ground truth, we encode weighted particle populations directly rather than binned aerosol states; size-resolved mass and number distributions serve only as diagnostic targets and visual summaries. The latent space accurately reconstructs these distributions, CCN spectra, optical coefficients, and immersion-freezing behavior while preserving the linear population structure needed for hybrid ML-physics models. Although the experiments focus on diagnostic reconstruction, the embedding is designed so that emissions and mixing can be represented exactly and nonlinear microphysical processes learned in a controlled latent space. This work establishes a foundation for learning aerosol-process evolution directly in latent space.
Comments34 pages, 12 figures
