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<strong class="journal-contentHeaderColor">Abstract.</strong> The hygroscopic properties of atmospheric aerosols are crucial for quantifying their impact on radiation and cloud formation. They are often characterized by a growth factor probability density function (GF-PDF), which can be parameterized as a superposition of multiple Gaussian distributions. Conventionally, nonparametric inversion methods are developed to retrieve GF-PDF from the instrument responses, e.g., measurements of humidified tandem differential mobility analyzer. However, additional parametric fittings are required to extract modal parameters from the inverted GF-PDF, a process that is computationally intensive and susceptible to fitting errors. In this study, we introduce Deep-GF-PRM, a deep learning framework that parameterizes the GF-PDF modal parameters directly from the instrument responses. The core of Deep-GF-PRM is a physics-informed neural network that embeds the instrument’s kernel function and physical constraints, creating end-to-end mapping of the GF-PDF modal parameters to the instrument response. Trained on a large dataset of synthetic instrument responses generated using a wide range of GF-PDFs and noise levels, Deep-GF-PRM accurately reproduces synthetic GF-PDFs and retrieves modal parameters with higher fidelity than conventional fitting approaches. The model is applied to real-world measurements, and yields results highly consistent with nonparametric inversions. Deep-GF-PRM thus provides an efficient and unsupervised solution for parameterizing aerosol hygroscopic properties.