FINN: Forest Informed Neural Networks

FINN is a differentiable forest gap model. A forest is represented as cohorts of same-species, same-size trees that are updated each timestep by four demographic processes — competition, growth, mortality and regeneration. Each process can be a mechanistic function, a neural network, or a mixture of the two, and the whole model is calibrated end-to-end by gradient descent through the simulation (implemented in torch).

Getting started

• finn() assembles a model from one process per component, each built with createProcess() (mechanistic) or createHybrid() (neural network).

• simulateForest() runs a model forward from known parameters.

• fit() calibrates a model to data; predict.finn_class() scores it.

• makeObsData(), resolveSiteIDs() and makeInitCohorts() turn a raw tree list into FINN's input tables.

• ALE(), summary.finn_class(), feature_importance() and conditionalEffects() interpret a fitted model.

The vignettes give a guided tour: browseVignettes("FINN").

See also

Useful links:

• https://github.com/FINNverse/FINN

• https://finnverse.github.io/FINN/

• Report bugs at https://github.com/FINNverse/FINN/issues

Author

Maintainer: Yannek K<c3><a4>ber y.kaeber@posteo.de (ORCID)

Authors:

• Maximilian Pichler maximilian.pichler@biologie.uni-regensburg.de (ORCID)