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About An Application in Lake Temperature Modeling
This paper introduces a novel framework called physics-guided neural networks (PGNN) that combines scientific knowledge from physics-based models with neural networks. PGNN leverages the output of physics-based model simulations along with observational features to generate predictions using a neural network. It also presents using physics-based loss functions in the neural network's learning objective to ensure predictions are consistent with known physics. The paper illustrates PGNN on lake temperature modeling, using physical relationships between temperature, density and depth to design a physics-based loss function.
Detailed Information
Author: | ['rand'] |
---|---|
Publication Year: | 2021 |
Pages: | 11 |
Language: | English |
Format: | |
Price: | FREE |
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