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SciML / ModelingToolkitNeuralNets.jl

Symbolic-Numeric Universal Differential Equations for Automating Scientific Machine Learning (SciML)
MIT License
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docs: use output of the neural network for plotting #30

Closed sathvikbhagavan closed 2 months ago

sathvikbhagavan commented 2 months ago

This fits the data correctly.

image

using solution to get the derivatives give slightly different results (I guess it might be because of splines):

image

codecov[bot] commented 2 months ago

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 86.66%. Comparing base (c7b07d5) to head (72fb7cf).

Additional details and impacted files ```diff @@ Coverage Diff @@ ## main #30 +/- ## ============================================ - Coverage 100.00% 86.66% -13.34% ============================================ Files 2 2 Lines 15 15 ============================================ - Hits 15 13 -2 - Misses 0 2 +2 ``` | [Flag](https://app.codecov.io/gh/SciML/ModelingToolkitNeuralNets.jl/pull/30/flags?src=pr&el=flags&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=SciML) | Coverage Δ | | |---|---|---| | [docs](https://app.codecov.io/gh/SciML/ModelingToolkitNeuralNets.jl/pull/30/flags?src=pr&el=flag&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=SciML) | `86.66% <ø> (ø)` | | Flags with carried forward coverage won't be shown. [Click here](https://docs.codecov.io/docs/carryforward-flags?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=SciML#carryforward-flags-in-the-pull-request-comment) to find out more.

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