1 citations · 1 across the 3 of their papers we have counts for
5 papers
Learning Aerosol Coagulation Dynamics in Latent Space with a Scale-Covariant Neural ODE
Wenhan Tang, Ruqi Yang, Jeffrey H. Curtis +5
Particle-resolved aerosol models preserve the joint size-composition structure that governs cloud activation, optical properties, and freezing, but their computational cost limits…
Reconstructing the Aerosol State from Partial Observations with Generative Modeling
E. Saleh, S. Ghaffari, J. H. Curtis +4
Key aerosol properties that shape climate -- such as CCN activity, scattering and absorption, and ice nucleation efficiency -- are difficult to infer from measurements that typical…
Uncertainty Quantification in Reduced-Order Gas-Phase Atmospheric Chemistry Modeling using Ensemble SINDy
Lin Guo, Xiaokai Yang, Zhonghua Zheng +2
Uncertainty quantification during atmospheric chemistry modeling is computationally expensive as it typically requires a large number of simulations using complex models. As large-…
Atmospheric chemistry surrogate modeling with sparse identification of nonlinear dynamics
Xiaokai Yang, Lin Guo, Zhonghua Zheng +2
Modeling atmospheric chemistry is computationally expensive and limits the widespread use of atmospheric chemical transport models. This computational cost arises from solving high…
Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields
Manho Park, Zhonghua Zheng, Nicole Riemer +1
We developed and applied a machine-learned discretization for one-dimensional (1-D) horizontal passive scalar advection, which is an operator component common to all chemical trans…