DOE OSTI · code-186205
mphys-surrogate-model
Abstract
This repository contains python scripts for building and studying reduced-order-modeling representations of droplet coalescence for eventual use in atmospheric models. The included data are generated from high-fidelity superdroplet methods and are utilized by machine learning pipelines to build data-driven models of droplet size distributions that evolve under coalescence. This repository further includes scripts to determine prediction (uncertainty) intervals on the data-driven model products based on conformal prediction.
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Katona, JonasE [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)], Gunawardena, NipunU [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)], De Jong, EmilyK [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)]. 2025-08-25. mphys-surrogate-model. https://doi.org/10.11578/dc.20260717.3
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