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Symbolic-Numeric Neural DAEs and Universal Differential Equations for Automating Scientific Machine Learning (SciML)
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SciML/ModelingToolkitNeuralNets.jl
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ModelingToolkitNeuralNets.jl is a package to create neural network blocks defined similar to MTKStandardLibrary components, to use them for solving Universal Differential Equations. It can be plugged to any part of the equations in an ODESystem usingRealInputArray andRealOutputArray components which gives a lot of flexibility to add the missing physics only to a part of the model.
For information on using the package,see the stable documentation. Use thein-development documentation for the version of the documentation, which contains the unreleased features.
TheNeuralNetworkBlock no longer usesRealInputArray &RealOutputArray,the ports are nowinputs andoutputs and they are normal vector variables.This simplifies the usage a bit and removes the need for the ModelingToolkitStandardLibrary dependency.
This version also moves to ModelingToolkit@v10.
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Symbolic-Numeric Neural DAEs and Universal Differential Equations for Automating Scientific Machine Learning (SciML)
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