https://github.com/mikhailmints/aerosolactivationemulation

https://github.com/mikhailmints/aerosolactivationemulation

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• Host: GitHub
• URL: https://github.com/mikhailmints/aerosolactivationemulation
• Owner: mikhailmints
• Created: 2023-07-07T18:17:38.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-04T01:40:22.000Z (over 1 year ago)
• Last Synced: 2023-09-04T21:56:04.303Z (over 1 year ago)
• Language: Python
• Size: 193 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
This repository contains code for generating datasets of parcel model runs using [PySDM](https://github.com/open-atmos/PySDM), and using them to train machine learning emulators of aerosol activation, using approaches based on the work of [Silva et al.](https://doi.org/10.5194/gmd-14-3067-2021)

To generate a dataset on Caltech's HPC cluster, run the following:

```bash

sbatch generate_parcel_data.sh [dataset_name] [num_simulations] [num_modes]

```

And then to see the output logs displayed,

```bash

tail -f slurm.out

```

For instance,

```bash

sbatch generate_parcel_data.sh my_2modal_dataset 20000 2

```

will perform 20000 runs of simulations with 2-modal aerosol populations, creating the files `datasets/my_2modal_dataset_train.csv`, `datasets/my_2modal_dataset_test.csv`, and `datasets/my_2modal_dataset_fail.csv` - which are the generated train dataset, test dataset, and initial conditions of the runs that failed (due to a condensation solver failure or timeout).