Pages that link to "Item:Q5029513"
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The following pages link to Physics-guided deep learning for generating turbulent inflow conditions (Q5029513):
Displaying 10 items.
- RANS turbulence model development using CFD-driven machine learning (Q777616) (← links)
- Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons (Q5038552) (← links)
- Deep learning in turbulent convection networks (Q5218582) (← links)
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance (Q5360504) (← links)
- A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers (Q5878764) (← links)
- Physics-informed neural networks for the Reynolds-averaged Navier-Stokes modeling of Rayleigh-Taylor turbulent mixing (Q6060732) (← links)
- An efficient and low-divergence method for generating inhomogeneous and anisotropic turbulence with arbitrary spectra (Q6178500) (← links)
- Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation (Q6557784) (← links)
- Enhancing CFD Solver with machine learning techniques (Q6588283) (← links)
- Generating synthetic turbulence with vector autoregression of proper orthogonal decomposition time coefficients (Q6659614) (← links)
