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20142021
most citedPhysics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

1.1k citations · 3.6k across the 20 of their papers we have counts for

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physics.flu-dyn2018★ 135 cited

Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks

Nicholas Geneva, Nicholas Zabaras

Data-driven methods for improving turbulence modeling in Reynolds-Averaged Navier-Stokes (RANS) simulations have gained significant interest in the computational fluid dynamics com…