Adding new tutorial notebook for internal inversion function calls.#52
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evan-greenbrg wants to merge 6 commits intoisofit:mainfrom
Open
Adding new tutorial notebook for internal inversion function calls.#52evan-greenbrg wants to merge 6 commits intoisofit:mainfrom
evan-greenbrg wants to merge 6 commits intoisofit:mainfrom
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jammont
reviewed
Apr 13, 2026
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+592
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+595
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", |
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Notebook outputs need to be purged before saving so it's not uploaded to the repo
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Purged, thanks for catching!
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Adds a new notebook.
Walks through how to use an existing config to run OE, AOE, and algebraic inversions.