Process‐Informed Neural Networks: A Hybrid Modelling Approach to Improve Predictive Performance and Inference of Neural Networks in Ecology and Beyond
Marieke Wesselkamp, Niklas Moser, Maria Kalweit, Joschka Boedecker, Carsten F Dormann · 2024
Read Process‐Informed Neural Networks: A Hybrid Modelling Approach to Improve Predictive Performance and Inference of Neural Networks in Ecology and Beyond by Marieke Wesselkamp, Niklas Moser, Maria Kalweit, Joschka Boedecker, Carsten F Dormann (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11613309.txt.
Description
Despite deep learning being state of the art for data‐driven model predictions, its application in ecology is currently subject to two important constraints: (i) deep‐learning methods are powerful in data‐rich regimes, but in ecology data are typically sparse; and (ii) deep‐learning models are black‐box methods and inferring the processes they represent are non‐trivial to elicit.
Catalog record
| CREATOR | Marieke Wesselkamp, Niklas Moser, Maria Kalweit, Joschka Boedecker, Carsten F Dormann |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |