graphorge

Getting started

  • Overview
  • Installation

Examples

  • Basic workflow
  • Graphorge example directory
  • A surrogate to estimate the knock-down factor of imperfect shells
  • A surrogate to estimate the flow rate through a porous medium

API

  • Code

License

  • MIT License
graphorge
  • Overview: module code

All modules for which code is available

  • collections
  • gnn_base_model.model.gnn_architectures
  • gnn_base_model.model.gnn_epd_model
  • gnn_base_model.model.gnn_model
  • gnn_base_model.predict.prediction
  • gnn_base_model.train.torch_loss
  • gnn_base_model.train.training
  • graphorge.gnn_base_model.data.graph_data
  • graphorge.gnn_base_model.data.graph_dataset
  • graphorge.gnn_base_model.model.gnn_architectures
  • graphorge.gnn_base_model.model.gnn_epd_model
  • graphorge.gnn_base_model.model.gnn_model
  • graphorge.gnn_base_model.model.model_summary
  • graphorge.gnn_base_model.optimization.hydra_optimization_plots
  • graphorge.gnn_base_model.optimization.hydra_optimization_template
  • graphorge.gnn_base_model.predict.prediction
  • graphorge.gnn_base_model.predict.prediction_plots
  • graphorge.gnn_base_model.train.cross_validation
  • graphorge.gnn_base_model.train.torch_loss
  • graphorge.gnn_base_model.train.training
  • graphorge.gnn_base_model.train.training_plots
  • graphorge.ioput.iostandard
  • graphorge.ioput.plots
  • graphorge.utilities.data_scalers
  • ioput.iostandard
  • ioput.plots
  • utilities.data_scalers

© Copyright 2023, Bernardo Ferreira.

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