What is it about?
Leveraging a Multilayer Perceptron, for both interpolation and extrapolation estimations and various grid resolutions, our findings demonstrate the Perceptron's prowess as a swift and accurate surrogate for traditional numerical methods.
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Why is it important?
Overall, the results of this work mark a pioneering step towards leveraging Machine Learning for modelling complex relationships in fluids phenomena, promising transformative advancements in Computational Fluid Dynamics.
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This page is a summary of: Forecasting two-dimensional channel flow using machine learning, Physics of Fluids, October 2024, American Institute of Physics,
DOI: 10.1063/5.0231005.
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