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File Summary
Shane edited this page Aug 8, 2020
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5 revisions
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config.py
: Configuration file containing project directives for file and folder names, constants for the geometry and chemistry of the problem, plot customizations, and so forth. -
utils.py
: Utilities for logging and timing operations, loading and saving data, saving figures, and calculating ROM reconstruction errors. -
chemistry_conversions.py
: Chemistry-related data conversions. -
data_processing.py
: Tools for (un)lifting and (un)scaling data.
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step1_unpack.py
: Read raw.tar
archives and compile snapshot data into a single HDF5 data set. -
step2_preprocess.py
: Lift, scale, and project GEMS training data. The process can also be decoupled into the following routines.-
step2a_transform.py
: Lift and scale GEMS snapshots. -
step2b_basis.py
: Compute the POD basis from lifted, scaled snapshots. -
step2c_project.py
: Project lifted, scaled training data onto low-dimensional subspaces.
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step3_train.py
: Learn ROMs from projected data via regularized Operator Inference.
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log.log
: Logging files (created as needed). For experiments with a specified number of training snapshots k, a separate log file is created (e.g., k = 10,000 activity is logged toconfig.BASE_FOLDER/k10000/log.log
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requirements.txt
: Python package requirements for this repository. Usepython3 -m pip install --user -r requirements.txt
to install the prerequisites.
Problem Statement: computational domain, state variables, and description of the data.
Installation and Setup: how to download the source code and the data files.
File Summary: short descriptions of each file in the repository.
Documentation: how to use the repository for reduced-order model learning.
Results: plots and figures, including many additional results that are not in the publications.
References: short list of primary references.