8 citations · 9 across the 5 of their papers we have counts for
5 papers
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…
Learned 1-D advection solver to accelerate air quality modeling
Manho Park, Zhonghua Zheng, Nicole Riemer +1
Accelerating the numerical integration of partial differential equations by learned surrogate model is a promising area of inquiry in the field of air pollution modeling. Most prev…
Orders-of-magnitude speedup in atmospheric chemistry modeling through neural network-based emulation
Makoto M. Kelp, Christopher W. Tessum, Julian D. Marshall
Chemical transport models (CTMs), which simulate air pollution transport, transformation, and removal, are computationally expensive, largely because of the computational intensity…