FINN: Forest Informed Neural Networks

Résumé fourni par la source

A hybrid dynamic forest (gap) model (FINN) that can be configured as a fully mechanistic, process-based model, like classic forest gap models, or with its demographic processes (growth, mortality, regeneration) replaced by deep neural networks (DNNs), or any combination of the two. Provides functions to define a model and its mechanistic or empirical components, calibrate it to forest inventory data, and interpret the calibrated processes. FINN is implemented with the 'torch' package, which supplies GPU support and the automatic differentiation used to calibrate the model by stochastic gradient descent; no knowledge of 'torch' is required. The hybrid modeling approach is described in Pichler and Käber (2026) .

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.