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20152023
most citedSurrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data

1.1k citations · 5.1k across the 37 of their papers we have counts for

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Showing 2015 · physics.comp-phShow all

2 papers · 2 filters

physics.comp-ph2015★ 316 cited

Quantifying and Reducing Model-Form Uncertainties in Reynolds-Averaged Navier-Stokes Simulations: A Data-Driven, Physics-Based Bayesian Approach

H. Xiao, J. -L. Wu, J. -X. Wang +2

Despite their well-known limitations, Reynolds-Averaged Navier-Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering application.…

physics.comp-ph2015★ 12 cited

Propagation of Input Uncertainty in Presence of Model-Form Uncertainty: A Multi-fidelity Approach for CFD Applications

Jian-xun Wang, Christopher J. Roy, Heng Xiao

Proper quantification and propagation of uncertainties in computational simulations are of critical importance. This issue is especially challenging for CFD applications. A particu…