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physics.flu-dyn2025
Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
Vivek Oommen, Siavash Khodakarami, Aniruddha Bora +2
Neural operators are promising surrogates for dynamical systems but when trained with standard L2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that…
physics.flu-dyn2024
Deep operator learning-based surrogate models for aerothermodynamic analysis of AEDC hypersonic waverider
Khemraj Shukla, Jasmine Ratchford, Luis Bravo +4
Neural networks are universal approximators that traditionally have been used to learn a map between function inputs and outputs. However, recent research has demonstrated that dee…