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Exploring the efficacy of a hybrid approach with modal decomposition over fully deep learning models for flow dynamics forecasting
Fluid dynamics problems are characterized by being multidimensional and nonlinear, causing the experiments and numerical simulations being complex, time-consuming and monetarily expensive.
A predictive physics-aware hybrid reduced order model for reacting flows
The number of degrees of freedom is reduced from thousands of temporal points to a few POD modes with their corresponding temporal coefficients.
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