199 citations · 302 across the 61 of their papers we have counts for
8 papers · 1 filter
Well-posedness of neural turbulence closures and tangent dissipation
Zhen Zhang, George Em Karniadakis
A neural turbulence closure defines a new boundary-value problem, , with a coupled Jacobian , where is the original mean-flow operator and $…
Generalizable turbulence closures across bluff-body shapes by PINN-based solver-agnostic training
Zhen Zhang, Theo Käufer, Louise Ronglan +2
Data-driven turbulence closures are commonly calibrated by embedding a CFD solver within an inverse procedure that requires a converged flow solution at every optimization iteratio…
GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics
Suguru Shiratori, Elham Kiyani, Khemraj Shukla +1
We develop a data-driven framework for discovering constitutive relations in models of fluid flow and scalar transport. Under the assumption that velocity and/or scalar fields are…
Effect of Turbulence-Closure Consistency on Airfoil Identification
Zhen Zhang, George Em Karniadakis
We consider an inverse flow problem in which the airfoil shape is identified from its wake signature, namely the velocity field in the wake of a target airfoil. This is an ill-pose…
Body-Free Simulation of Three-Dimensional Turbulent Cylinder Wakes
Zhicheng Wang, Theo Käufer, Khemraj Shukla +2
We present a body-free simulation framework for three-dimensional turbulent cylinder wakes, in which the upstream cylinder is not explicitly resolved. Instead, the incompressible N…
Turbulence Closure in RANS and Flow Inference around a Cylinder using PINNs and Sparse Experimental Data
Z. Zhang, K. Shukla, Z. Wang +8
Traditional Reynolds-averaged Navier-Stokes (RANS) closures, based on the Boussinesq eddy viscosity hypothesis and calibrated on canonical flows, often yield inaccurate predictions…
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…
PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction
Maximilian Dreisbach, Elham Kiyani, Jochen Kriegseis +2
Two-phase flow phenomena underpin critical technologies such as hydrogen fuel cells, spray cooling, and combustion, where droplet dynamics govern performance and efficiency. Conven…