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Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Using Generative Art to Convey Past and Future Climate Transitions
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Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction.

Yuxin Meng, Eric Rigall, Xueen Chen

    IEEE Transactions on Neural Networks and Learning Systems
    |November 10, 2021
    PubMed
    Summary

    This study introduces a novel framework combining physics-based numerical models and generative adversarial networks (GANs) to improve sea subsurface temperature prediction. The hybrid approach enhances accuracy by integrating physical principles with data-driven pattern recognition for climate change research.

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    Using Generative Art to Convey Past and Future Climate Transitions
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    Area of Science:

    • Oceanography
    • Climate Science
    • Artificial Intelligence

    Background:

    • Sea subsurface temperature is crucial for marine ecosystems and heat transfer.
    • Global warming significantly impacts sea subsurface temperature.
    • Current prediction models are either physics-based or data-driven, with limited integration.

    Purpose of the Study:

    • To develop a hybrid framework for enhanced sea subsurface temperature prediction.
    • To combine the strengths of physics-based numerical models and machine learning (GANs).
    • To improve the accuracy and extrapolation capabilities of subsurface temperature forecasts.

    Main Methods:

    • A generative adversarial network (GAN) was employed to learn simplified physics.
    • The GAN model was integrated with a numerical model for subsurface temperature prediction.
    • Observation data were used to calibrate the GAN model parameters.

    Main Results:

    • The proposed framework demonstrated superior performance in predicting daily sea subsurface temperature.
    • Experiments in the South China Sea validated the effectiveness of the hybrid approach.
    • The combined method outperformed existing state-of-the-art prediction techniques.

    Conclusions:

    • Integrating physics-based and data-driven methods offers significant advantages for sea subsurface temperature prediction.
    • The novel GAN-numerical model framework provides a more robust and accurate forecasting tool.
    • This approach has implications for understanding climate change impacts on marine environments.