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 industrial applications such as natural language translation and image classification, using billions or even trillions of data samples. Accurate water temperatures are critical to understanding contemporary change, and for predicting future thermal habitat of economically valuable fish.
|Title||Physics-guided neural networks (PGNN): An application in lake temperature modeling|
|Authors||Arka Daw, Anuj Karpatne, William Watkins, Jordan Read, Vipin Kumar|
|Publication Type||Book Chapter|
|Publication Subtype||Book Chapter|
|Record Source||USGS Publications Warehouse|
|USGS Organization||WMA - Integrated Information Dissemination Division|