Bayesian neural networks correct RANS turbulence models via kinetic energy source terms and anisotropy tensors, improving velocity predictions on training flows but showing reduced accuracy and under-coverage on unseen separated flows.
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DIANO builds coarse-grid latent spaces for fluid dynamics data via neural operator encoding and decoding while integrating a differentiable PDE solver directly in the latent space for end-to-end physics-constrained training.
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Bayesian neural network correction of RANS turbulence models with uncertainty quantification in separated flows
Bayesian neural networks correct RANS turbulence models via kinetic energy source terms and anisotropy tensors, improving velocity predictions on training flows but showing reduced accuracy and under-coverage on unseen separated flows.
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Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling
DIANO builds coarse-grid latent spaces for fluid dynamics data via neural operator encoding and decoding while integrating a differentiable PDE solver directly in the latent space for end-to-end physics-constrained training.