Physics-Guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
University of Minnesota System · Virginia Tech · +1 more institution
Abstract
This chapter introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. It explains termed physics-guided neural networks (PGNN), leverages the output of physics-based model simulations along with observational features in a hybrid modeling setup to generate predictions using a neural network architecture. Data science has become an indispensable tool for knowledge discovery in the era of big data, as the volume of data continues to explode in practically every research domain. Recent advances in data science such as deep learning have been immensely successful in transforming the state-of-the-art in a number of commercial and…
Citation impact
- FWCI
- 121.89
- Percentile
- 100%
- References
- 25
Authors
5- ADArka DawCorresponding
University of Minnesota System, Virginia Tech, United States Geological Survey
- AKAnuj Karpatne
University of Minnesota System, United States Geological Survey, Virginia Tech
- WDW D Watkins
United States Geological Survey, Virginia Tech, University of Minnesota System
- JSJordan S. Read
- VKVipin Kumar
University of Minnesota System, United States Geological Survey, Virginia Tech
Topics & keywords
- Artificial neural network
- Physics
- Computer science
- Environmental science
- Statistical physics
- Artificial intelligence