Physics-guided neural networks (PGNN): An application in lake temperature modeling

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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.
Publication type Book chapter
Publication Subtype Book Chapter
Title Physics-guided neural networks (PGNN): An application in lake temperature modeling
Chapter 15
DOI 10.1201/9781003143376-15
Year Published 2022
Language English
Publisher Taylor & Francis
Contributing office(s) WMA - Integrated Information Dissemination Division
Description 20 p.
Larger Work Type Book
Larger Work Subtype Monograph
Larger Work Title Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data
First page 353
Last page 372
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