/GeometricMachineLearning.jl

Structure Preserving Machine Learning Models in Julia

Primary LanguageJuliaMIT LicenseMIT

Geometric Machine Learning

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GeometricMachineLearning.jl offers a flexible tool for designing neural networks for dynamical systems with geometric structure, such as Hamiltonian (symplectic) or Lagrangian (variational) systems.

At its core every neural network comprises three components: a neural network architecture, a loss function and an optimizer.

Traditionally, physical properties have been encoded into the loss function (PiNN approach), but in GeometricMachineLearning.jl this is exclusively done through the architectures and the optimizers of the neural network, thus giving theoretical guarantees that these properties are actually preserved.