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Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling

Citation

Karpatne, Anuj, Watkins, William, Read, Jordan, and Kumar, Vipin, Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling: .

Summary

Abstract (from arXiv): This paper introduces a novel framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. This framework, termed as physics-guided neural network (PGNN), leverages the output of physics-based model simulations along with observational features to generate predictions using a neural network architecture. Further, this paper presents a novel framework for using physics-based loss functions in the learning objective of neural networks, to ensure that the model predictions not only show lower errors on the training set but are also scientifically consistent with the known physics on the unlabeled set. We illustrate the effectiveness of PGNN for the problem [...]

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  • National and Regional Climate Adaptation Science Centers
  • Northeast CASC

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Water, Coasts and Ice
Wildlife and Plants
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journalarXiv

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